{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "fc769377-6253-43c4-9afb-5b2b8e63726d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from xgboost import XGBClassifier\n",
    "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n",
    "from scipy.stats import chi2_contingency"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00c1216b-84bd-4a6a-bec8-f0ecf9007809",
   "metadata": {},
   "source": [
    "# 1 Import Data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47cf1726-ad76-44e2-851b-757d556b8e19",
   "metadata": {},
   "source": [
    "The \"ObesityDataSet_raw_and_data_sinthetic.csv\" file include data for the estimation of obesity levels in individuals from the countries of Mexico, Peru and Colombia, based on their eating habits and physical condition. The data contains 17 attributes and 2111 records, the records are labeled with the class variable NObesity (Obesity Level), that allows classification of the data using the values of Insufficient Weight, Normal Weight, Overweight Level I, Overweight Level II, Obesity Type I, Obesity Type II and Obesity Type III. The variable information is as follows  \n",
    "\n",
    "**Notes:**  \n",
    "- 77% of the data was generated synthetically using the Weka tool and the SMOTE filter, 23% of the data was collected directly from users through a web platform.\n",
    "- The synthasized data fits the distribution of the data but generates floats (ratio data) where ordinal and integer data were the original values in the survey.\n",
    "- The target variable is comprised of the variables height and weight. Height and weight are used to classisfy a persons BMI. The BMI measure was then used to classify each observation."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1db8d131-7337-4ec8-8cdb-a777528ee8c6",
   "metadata": {},
   "source": [
    "| Category                | Variable Name                 | Role    | Type        | Description                                                                     | Answers                                                                 |\n",
    "|----------|--------------------|---------|-------------|---------------------------------------------------------------------------------|----------------------------------------------------------------------------------|\n",
    "| Responder Characteristics | Gender                       | Feature | Categorical | What is your gender?                                                            | Female<br>Male                                                                   |\n",
    "|| Age                          | Feature | Continuous  | What is your age?                                                               | Numeric value                                                                    |\n",
    "|| Height                       | Feature | Continuous  | What is your height?                                                            | Numeric value in meters                                                          |\n",
    "|| Weight                       | Feature | Continuous  | What is your weight?                                                            | Numeric value in kilograms                                                       |\n",
    "|| family_history_with_overweight | Feature | Binary       | Has a family member suffered or suffers from overweight?                        | Yes<br>No                                                                        |\n",
    "|Eating Habits    | FAVC                         | Feature | Binary       | Do you eat high caloric food frequently?                                        | Yes<br>No                                                                        |\n",
    "|| FCVC                         | Feature | Continuous  | Do you usually eat vegetables in your meals?                                    | Never<br>Sometimes<br>Always                                                     |\n",
    "|| NCP                          | Feature | Continuous  | How many main meals do you have daily?                                          | One<br>Two<br>Three<br>More than three                                      |\n",
    "|| CAEC                         | Feature | Categorical | Do you eat any food between meals?                                              | No<br>Sometimes<br>Frequently<br>Always                                          |\n",
    "|| SMOKE                        | Feature | Binary       | Do you smoke?                                                                   | Yes<br>No                                                                        |\n",
    "|| CH2O                         | Feature | Continuous  | How much water do you drink daily?                                              | Less than a liter<br>Between 1 and 2 L<br>More than 2 L                          |\n",
    "|| CALC                         | Feature | Categorical | How often do you drink alcohol?                                                 | I do not drink<br>Sometimes<br>Frequently<br>Always                              |\n",
    "| Physical Conditioning | SCC                          | Feature | Binary       | Do you monitor the calories you eat daily?                                      | Yes<br>No                                                                        |\n",
    "|| FAF                          | Feature | Continuous  | How often do you have physical activity?                                        | I do not have<br>1 or 2 days<br>2 or 4 days<br>4 or 5 days                        |\n",
    "|| TUE                          | Feature | Continuous  | How much time do you use technological devices (e.g. phone, TV)?               | 0–2 hours<br>3–5 hours<br>More than 5 hours                                      |\n",
    "|| MTRANS                       | Feature | Categorical | Which transportation do you usually use?                                        | Automobile<br>Motorbike<br>Bike<br>Public Transportation<br>Walking              |\n",
    "|Target Variable| NObeyesdad                   | Target  | Categorical | Obesity level                                                                   | Insufficient_Weight<br>Normal_Weight<br>Overweight_Level_I<br>Overweight_Level_II<br>Obesity_Type_I<br>Obesity_Type_II<br>Obesity_Type_III |\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c404c223-0d4f-408b-9936-e18f9985ccc9",
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    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>family_history_with_overweight</th>\n",
       "      <th>FAVC</th>\n",
       "      <th>FCVC</th>\n",
       "      <th>NCP</th>\n",
       "      <th>CAEC</th>\n",
       "      <th>SMOKE</th>\n",
       "      <th>CH2O</th>\n",
       "      <th>SCC</th>\n",
       "      <th>FAF</th>\n",
       "      <th>TUE</th>\n",
       "      <th>CALC</th>\n",
       "      <th>MTRANS</th>\n",
       "      <th>NObeyesdad</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Female</td>\n",
       "      <td>21.0</td>\n",
       "      <td>1.62</td>\n",
       "      <td>64.0</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Sometimes</td>\n",
       "      <td>no</td>\n",
       "      <td>2.0</td>\n",
       "      <td>no</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>no</td>\n",
       "      <td>Public_Transportation</td>\n",
       "      <td>Normal_Weight</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Female</td>\n",
       "      <td>21.0</td>\n",
       "      <td>1.52</td>\n",
       "      <td>56.0</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
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       "      <td>yes</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Sometimes</td>\n",
       "      <td>Public_Transportation</td>\n",
       "      <td>Normal_Weight</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Male</td>\n",
       "      <td>23.0</td>\n",
       "      <td>1.80</td>\n",
       "      <td>77.0</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Sometimes</td>\n",
       "      <td>no</td>\n",
       "      <td>2.0</td>\n",
       "      <td>no</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Frequently</td>\n",
       "      <td>Public_Transportation</td>\n",
       "      <td>Normal_Weight</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>1.80</td>\n",
       "      <td>87.0</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Sometimes</td>\n",
       "      <td>no</td>\n",
       "      <td>2.0</td>\n",
       "      <td>no</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Frequently</td>\n",
       "      <td>Walking</td>\n",
       "      <td>Overweight_Level_I</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1.78</td>\n",
       "      <td>89.8</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Sometimes</td>\n",
       "      <td>no</td>\n",
       "      <td>2.0</td>\n",
       "      <td>no</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Sometimes</td>\n",
       "      <td>Public_Transportation</td>\n",
       "      <td>Overweight_Level_II</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Gender   Age  Height  Weight family_history_with_overweight FAVC  FCVC  \\\n",
       "0  Female  21.0    1.62    64.0                            yes   no   2.0   \n",
       "1  Female  21.0    1.52    56.0                            yes   no   3.0   \n",
       "2    Male  23.0    1.80    77.0                            yes   no   2.0   \n",
       "3    Male  27.0    1.80    87.0                             no   no   3.0   \n",
       "4    Male  22.0    1.78    89.8                             no   no   2.0   \n",
       "\n",
       "   NCP       CAEC SMOKE  CH2O  SCC  FAF  TUE        CALC  \\\n",
       "0  3.0  Sometimes    no   2.0   no  0.0  1.0          no   \n",
       "1  3.0  Sometimes   yes   3.0  yes  3.0  0.0   Sometimes   \n",
       "2  3.0  Sometimes    no   2.0   no  2.0  1.0  Frequently   \n",
       "3  3.0  Sometimes    no   2.0   no  2.0  0.0  Frequently   \n",
       "4  1.0  Sometimes    no   2.0   no  0.0  0.0   Sometimes   \n",
       "\n",
       "                  MTRANS           NObeyesdad  \n",
       "0  Public_Transportation        Normal_Weight  \n",
       "1  Public_Transportation        Normal_Weight  \n",
       "2  Public_Transportation        Normal_Weight  \n",
       "3                Walking   Overweight_Level_I  \n",
       "4  Public_Transportation  Overweight_Level_II  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read the data with pandas\n",
    "df = pd.read_csv(\"ObesityDataSet_raw_and_data_sinthetic.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d88bdf27-7b17-4497-a6b1-06fc4db31b0f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2111, 17)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the number of instances and features\n",
    "row_num = df.shape[0]\n",
    "col_num = df.shape[1]\n",
    "row_num, col_num"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "fc1483ca-13bf-4d90-8e02-be26459e5b0a",
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   "outputs": [
    {
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       "      <td>2.000000</td>\n",
       "      <td>no</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>Walking</td>\n",
       "      <td>Overweight_Level_I</td>\n",
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       "      <th>4</th>\n",
       "      <td>Male</td>\n",
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       "      <td>1.780000</td>\n",
       "      <td>89.800000</td>\n",
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       "      <td>1.710730</td>\n",
       "      <td>131.408528</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
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       "      <td>no</td>\n",
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       "      <td>Female</td>\n",
       "      <td>21.982942</td>\n",
       "      <td>1.748584</td>\n",
       "      <td>133.742943</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
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       "      <td>3.0</td>\n",
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       "      <td>2.005130</td>\n",
       "      <td>no</td>\n",
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       "      <td>Obesity_Type_III</td>\n",
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       "      <td>22.524036</td>\n",
       "      <td>1.752206</td>\n",
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       "      <td>Obesity_Type_III</td>\n",
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       "      <td>Female</td>\n",
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       "      <td>1.739450</td>\n",
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       "      <td>yes</td>\n",
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       "      <td>3.0</td>\n",
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       "      <td>no</td>\n",
       "      <td>2.852339</td>\n",
       "      <td>no</td>\n",
       "      <td>1.139107</td>\n",
       "      <td>0.586035</td>\n",
       "      <td>Sometimes</td>\n",
       "      <td>Public_Transportation</td>\n",
       "      <td>Obesity_Type_III</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2110</th>\n",
       "      <td>Female</td>\n",
       "      <td>23.664709</td>\n",
       "      <td>1.738836</td>\n",
       "      <td>133.472641</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Sometimes</td>\n",
       "      <td>no</td>\n",
       "      <td>2.863513</td>\n",
       "      <td>no</td>\n",
       "      <td>1.026452</td>\n",
       "      <td>0.714137</td>\n",
       "      <td>Sometimes</td>\n",
       "      <td>Public_Transportation</td>\n",
       "      <td>Obesity_Type_III</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2111 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Gender        Age    Height      Weight FamilyHistory HighCalorieFood  \\\n",
       "0     Female  21.000000  1.620000   64.000000           yes              no   \n",
       "1     Female  21.000000  1.520000   56.000000           yes              no   \n",
       "2       Male  23.000000  1.800000   77.000000           yes              no   \n",
       "3       Male  27.000000  1.800000   87.000000            no              no   \n",
       "4       Male  22.000000  1.780000   89.800000            no              no   \n",
       "...      ...        ...       ...         ...           ...             ...   \n",
       "2106  Female  20.976842  1.710730  131.408528           yes             yes   \n",
       "2107  Female  21.982942  1.748584  133.742943           yes             yes   \n",
       "2108  Female  22.524036  1.752206  133.689352           yes             yes   \n",
       "2109  Female  24.361936  1.739450  133.346641           yes             yes   \n",
       "2110  Female  23.664709  1.738836  133.472641           yes             yes   \n",
       "\n",
       "      VegetablesPerDay  MainMealsPerDay      Snack Smoke  WaterIntake  \\\n",
       "0                  2.0              3.0  Sometimes    no     2.000000   \n",
       "1                  3.0              3.0  Sometimes   yes     3.000000   \n",
       "2                  2.0              3.0  Sometimes    no     2.000000   \n",
       "3                  3.0              3.0  Sometimes    no     2.000000   \n",
       "4                  2.0              1.0  Sometimes    no     2.000000   \n",
       "...                ...              ...        ...   ...          ...   \n",
       "2106               3.0              3.0  Sometimes    no     1.728139   \n",
       "2107               3.0              3.0  Sometimes    no     2.005130   \n",
       "2108               3.0              3.0  Sometimes    no     2.054193   \n",
       "2109               3.0              3.0  Sometimes    no     2.852339   \n",
       "2110               3.0              3.0  Sometimes    no     2.863513   \n",
       "\n",
       "     CaloriesMonitor  PhysicalActivity  TechUsageTime     Alcohol  \\\n",
       "0                 no          0.000000       1.000000          no   \n",
       "1                yes          3.000000       0.000000   Sometimes   \n",
       "2                 no          2.000000       1.000000  Frequently   \n",
       "3                 no          2.000000       0.000000  Frequently   \n",
       "4                 no          0.000000       0.000000   Sometimes   \n",
       "...              ...               ...            ...         ...   \n",
       "2106              no          1.676269       0.906247   Sometimes   \n",
       "2107              no          1.341390       0.599270   Sometimes   \n",
       "2108              no          1.414209       0.646288   Sometimes   \n",
       "2109              no          1.139107       0.586035   Sometimes   \n",
       "2110              no          1.026452       0.714137   Sometimes   \n",
       "\n",
       "              TransportMode         ObesityLevel  \n",
       "0     Public_Transportation        Normal_Weight  \n",
       "1     Public_Transportation        Normal_Weight  \n",
       "2     Public_Transportation        Normal_Weight  \n",
       "3                   Walking   Overweight_Level_I  \n",
       "4     Public_Transportation  Overweight_Level_II  \n",
       "...                     ...                  ...  \n",
       "2106  Public_Transportation     Obesity_Type_III  \n",
       "2107  Public_Transportation     Obesity_Type_III  \n",
       "2108  Public_Transportation     Obesity_Type_III  \n",
       "2109  Public_Transportation     Obesity_Type_III  \n",
       "2110  Public_Transportation     Obesity_Type_III  \n",
       "\n",
       "[2111 rows x 17 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Rename head\n",
    "df.rename(columns={\n",
    "    'family_history_with_overweight': 'FamilyHistory',\n",
    "    'FAVC': 'HighCalorieFood',\n",
    "    'FCVC': 'VegetablesPerDay',\n",
    "    'NCP': 'MainMealsPerDay',\n",
    "    'CAEC': 'Snack',\n",
    "    'SMOKE': 'Smoke',\n",
    "    'CH2O': 'WaterIntake',\n",
    "    'SCC': 'CaloriesMonitor',\n",
    "    'FAF': 'PhysicalActivity',\n",
    "    'TUE': 'TechUsageTime',\n",
    "    'CALC': 'Alcohol',\n",
    "    'MTRANS': 'TransportMode',\n",
    "    'NObeyesdad': 'ObesityLevel'\n",
    "}, inplace=True)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "861c82a5-51dc-4334-837f-cd896524e332",
   "metadata": {},
   "source": [
    "# 2 Data Progressing"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d9435cb-ff99-4c0c-9c46-2582eb03c2ac",
   "metadata": {},
   "source": [
    "## 2.1 Data Cleaning"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "80aa1282-ed5e-4c37-8480-a3385957300f",
   "metadata": {},
   "source": [
    "### 2.1.1 Exploring the dataset to find potential issues"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a8c282b-7f26-4702-ba6d-f029868595f9",
   "metadata": {},
   "source": [
    "- Row-ID Consistency  \n",
    "  The number of rows matches the ID count perfectly. However, since the dataset lacks explicit ID labels, duplicate entries cannot be ruled out.  \n",
    "\n",
    "- Missing Values Check    \n",
    "  All features have identical `count` values matching the total row count. Suggests no missing values in the dataset.  \n",
    "\n",
    "- Data Cleanliness   \n",
    "  Categorical values are well-formatted with no apparent irregularities or noise.  \n",
    "\n",
    "- Data Type Issues    \n",
    "   The following variables are stored as continuous but should be treated as categorical:  \n",
    "   - `VegetablesPerDay` (daily vegetable intake frequency)  \n",
    "   - `MainMealsPerDay` (daily main meals count)  \n",
    "   - `WaterIntake` (water consumption level)  \n",
    "   - `PhysicalActivity` (physical activity frequency)  \n",
    "   - `TechUsageTime` (technology usage duration)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "36dbd16f-2fc5-4425-a5da-f4bb131fadf9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>VegetablesPerDay</th>\n",
       "      <th>MainMealsPerDay</th>\n",
       "      <th>WaterIntake</th>\n",
       "      <th>PhysicalActivity</th>\n",
       "      <th>TechUsageTime</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2111.000000</td>\n",
       "      <td>2111.000000</td>\n",
       "      <td>2111.000000</td>\n",
       "      <td>2111.000000</td>\n",
       "      <td>2111.000000</td>\n",
       "      <td>2111.000000</td>\n",
       "      <td>2111.000000</td>\n",
       "      <td>2111.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>24.312600</td>\n",
       "      <td>1.701677</td>\n",
       "      <td>86.586058</td>\n",
       "      <td>2.419043</td>\n",
       "      <td>2.685628</td>\n",
       "      <td>2.008011</td>\n",
       "      <td>1.010298</td>\n",
       "      <td>0.657866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>6.345968</td>\n",
       "      <td>0.093305</td>\n",
       "      <td>26.191172</td>\n",
       "      <td>0.533927</td>\n",
       "      <td>0.778039</td>\n",
       "      <td>0.612953</td>\n",
       "      <td>0.850592</td>\n",
       "      <td>0.608927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>14.000000</td>\n",
       "      <td>1.450000</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>19.947192</td>\n",
       "      <td>1.630000</td>\n",
       "      <td>65.473343</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.658738</td>\n",
       "      <td>1.584812</td>\n",
       "      <td>0.124505</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>22.777890</td>\n",
       "      <td>1.700499</td>\n",
       "      <td>83.000000</td>\n",
       "      <td>2.385502</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.625350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>26.000000</td>\n",
       "      <td>1.768464</td>\n",
       "      <td>107.430682</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.477420</td>\n",
       "      <td>1.666678</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>61.000000</td>\n",
       "      <td>1.980000</td>\n",
       "      <td>173.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Age       Height       Weight  VegetablesPerDay  \\\n",
       "count  2111.000000  2111.000000  2111.000000       2111.000000   \n",
       "mean     24.312600     1.701677    86.586058          2.419043   \n",
       "std       6.345968     0.093305    26.191172          0.533927   \n",
       "min      14.000000     1.450000    39.000000          1.000000   \n",
       "25%      19.947192     1.630000    65.473343          2.000000   \n",
       "50%      22.777890     1.700499    83.000000          2.385502   \n",
       "75%      26.000000     1.768464   107.430682          3.000000   \n",
       "max      61.000000     1.980000   173.000000          3.000000   \n",
       "\n",
       "       MainMealsPerDay  WaterIntake  PhysicalActivity  TechUsageTime  \n",
       "count      2111.000000  2111.000000       2111.000000    2111.000000  \n",
       "mean          2.685628     2.008011          1.010298       0.657866  \n",
       "std           0.778039     0.612953          0.850592       0.608927  \n",
       "min           1.000000     1.000000          0.000000       0.000000  \n",
       "25%           2.658738     1.584812          0.124505       0.000000  \n",
       "50%           3.000000     2.000000          1.000000       0.625350  \n",
       "75%           3.000000     2.477420          1.666678       1.000000  \n",
       "max           4.000000     3.000000          3.000000       2.000000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "cabad9a8-c399-4023-b8c6-4b2f0d1b4f3d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "*******Gender*******\n",
      "['Female' 'Male']\n",
      "count     2111\n",
      "unique       2\n",
      "top       Male\n",
      "freq      1068\n",
      "Name: Gender, dtype: object\n",
      "\n",
      "*******FamilyHistory*******\n",
      "['yes' 'no']\n",
      "count     2111\n",
      "unique       2\n",
      "top        yes\n",
      "freq      1726\n",
      "Name: FamilyHistory, dtype: object\n",
      "\n",
      "*******HighCalorieFood*******\n",
      "['no' 'yes']\n",
      "count     2111\n",
      "unique       2\n",
      "top        yes\n",
      "freq      1866\n",
      "Name: HighCalorieFood, dtype: object\n",
      "*******Snack*******\n",
      "['Sometimes' 'Frequently' 'Always' 'no']\n",
      "count          2111\n",
      "unique            4\n",
      "top       Sometimes\n",
      "freq           1765\n",
      "Name: Snack, dtype: object\n",
      "\n",
      "*******Smoke*******\n",
      "['no' 'yes']\n",
      "count     2111\n",
      "unique       2\n",
      "top         no\n",
      "freq      2067\n",
      "Name: Smoke, dtype: object\n",
      "\n",
      "*******CaloriesMonitor*******\n",
      "['no' 'yes']\n",
      "count     2111\n",
      "unique       2\n",
      "top         no\n",
      "freq      2015\n",
      "Name: CaloriesMonitor, dtype: object\n",
      "*******Alcohol*******\n",
      "['no' 'Sometimes' 'Frequently' 'Always']\n",
      "count          2111\n",
      "unique            4\n",
      "top       Sometimes\n",
      "freq           1401\n",
      "Name: Alcohol, dtype: object\n",
      "\n",
      "*******TransportMode*******\n",
      "['Public_Transportation' 'Walking' 'Automobile' 'Motorbike' 'Bike']\n",
      "count                      2111\n",
      "unique                        5\n",
      "top       Public_Transportation\n",
      "freq                       1580\n",
      "Name: TransportMode, dtype: object\n",
      "\n",
      "*******ObesityLevel*******\n",
      "['Normal_Weight' 'Overweight_Level_I' 'Overweight_Level_II'\n",
      " 'Obesity_Type_I' 'Insufficient_Weight' 'Obesity_Type_II'\n",
      " 'Obesity_Type_III']\n",
      "count               2111\n",
      "unique                 7\n",
      "top       Obesity_Type_I\n",
      "freq                 351\n",
      "Name: ObesityLevel, dtype: object\n"
     ]
    }
   ],
   "source": [
    "print('*******Gender*******')\n",
    "print(df['Gender'].unique())\n",
    "print(df['Gender'].describe())\n",
    "print('\\n*******FamilyHistory*******')\n",
    "print(df['FamilyHistory'].unique())\n",
    "print(df['FamilyHistory'].describe())\n",
    "print('\\n*******HighCalorieFood*******')\n",
    "print(df['HighCalorieFood'].unique())\n",
    "print(df['HighCalorieFood'].describe())\n",
    "print('*******Snack*******')\n",
    "print(df['Snack'].unique())\n",
    "print(df['Snack'].describe())\n",
    "print('\\n*******Smoke*******')\n",
    "print(df['Smoke'].unique())\n",
    "print(df['Smoke'].describe())\n",
    "print('\\n*******CaloriesMonitor*******')\n",
    "print(df['CaloriesMonitor'].unique())\n",
    "print(df['CaloriesMonitor'].describe())\n",
    "print('*******Alcohol*******')\n",
    "print(df['Alcohol'].unique())\n",
    "print(df['Alcohol'].describe())\n",
    "print('\\n*******TransportMode*******')\n",
    "print(df['TransportMode'].unique())\n",
    "print(df['TransportMode'].describe())\n",
    "print('\\n*******ObesityLevel*******')\n",
    "print(df['ObesityLevel'].unique())\n",
    "print(df['ObesityLevel'].describe())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7eb7e675-f310-47fa-8942-9051a84b74d0",
   "metadata": {},
   "source": [
    "### 2.1.2 Dealing with duplicates"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d414a38-6e23-4875-a389-8ade860c1d72",
   "metadata": {},
   "source": [
    "Delete duplicate values and only retain the first record in each set of duplicate values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b0a562e8-b36e-40a7-90a7-b8184df186a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(33, 17)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.duplicated(keep=False)].shape # False: All repeated rows are marked as True, whether they are the first or subsequent repeated rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "6786d73e-ba7f-412b-b067-f26a6a001380",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2087, 17)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.drop_duplicates(keep='first').reset_index(drop=True) # Keep only the first record in each set of duplicate values, rebuild the index and discard the old index columns\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98a0f3e3-a9ee-421d-a7d7-f670c2d9d3d5",
   "metadata": {},
   "source": [
    "### 2.1.3 Dealing with outliers and bad data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7f9c41e-2ebf-4101-bc39-3d3fac983141",
   "metadata": {},
   "source": [
    "#### 2.1.3.1 IQR"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d440877-590f-4fe6-8bd4-a4e4b4bbe978",
   "metadata": {},
   "source": [
    "To assess potential outliers in the data, we applied the interquartile range (IQR) method to all numerical variables. The acceptable range was defined as $[Q1 - 1.5 \\times IQR, Q3 + 1.5 \\times IQR]$, with values outside this range considered outliers.\n",
    "\n",
    "For the Age variable, we identified 167 outliers, predominantly in individuals aged 35 and above. While these values were statistically classified as outliers, they represent valid age ranges in our population sample and were therefore retained for analysis.\n",
    "\n",
    "The Height variable contained one marginal case (1.98m) and the Weight variable had one extreme value (173kg). Although these measurements fall at the upper extremes of natural human distributions, they remain within biologically plausible ranges and were kept in the dataset.\n",
    "\n",
    "Based on domain knowledge and variable semantics, we made the decision to retain all statistically identified outliers. This approach preserves the dataset's original representativeness and ensures better generalization capability for model training. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f0f0484d-f12e-4622-86eb-1f65e484f3be",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "【Age】\n",
      "IQR: 6.084063\n",
      "Lower Bound: 10.789844\n",
      "Upper Bound: 35.126094\n",
      "Outliers Count: 167\n",
      "Outlier Values:\n",
      "[41.       52.       39.       55.       38.       61.       44.\n",
      " 36.       38.       40.       55.       45.       38.       39.\n",
      " 41.       41.       40.       51.       56.       39.       38.\n",
      " 38.       38.       41.       40.       37.       40.       37.\n",
      " 41.       36.       37.       38.       36.       45.       41.823567\n",
      " 36.769646 35.194089 37.218161 42.24475  37.455752 40.       38.943282\n",
      " 35.483601 40.       38.939448 39.965474 38.692265 38.952866 36.631456\n",
      " 39.214514 38.825189 35.217173 55.24625  42.189023 38.464538 37.496175\n",
      " 50.832559 36.310292 43.238402 45.       55.137881 38.378056 39.170029\n",
      " 46.491859 38.384177 37.205173 37.492444 35.456326 39.392569 37.275298\n",
      " 55.022494 41.743333 38.097395 37.642177 47.7061   35.719457 35.432059\n",
      " 39.585811 45.821267 39.759575 43.604901 42.31607  37.356288 40.821515\n",
      " 37.955371 40.317787 35.389491 40.951591 39.135634 37.832949 37.631769\n",
      " 37.524551 43.510672 36.726617 38.297259 42.337283 47.283374 38.148845\n",
      " 37.96543  43.591999 40.993179 37.597953 37.532066 36.023972 39.656559\n",
      " 35.322112 41.403862 40.702771 39.213399 38.098745 38.547267 38.683794\n",
      " 38.748307 39.685846 42.586285 43.719395 43.37634  39.648946 39.129291\n",
      " 37.441044 40.466313 38.445148 38.397463 37.974483 39.569004 36.67933\n",
      " 36.673882 37.936044 38.895069 40.789529 40.654155 38.542937 39.29266\n",
      " 37.872971 37.613378 37.471683 39.12631  37.063599 41.318302 43.726081\n",
      " 37.186795 41.       36.542885 40.50021  40.794057 37.056193 38.523646\n",
      " 38.71216  37.084742 39.08886  40.973007 41.       40.366238 40.106145\n",
      " 40.174191 37.588628 37.997912 39.825592 36.839761 40.564513 40.501722\n",
      " 37.638102 37.765356 37.207082 38.10894  38.644441 38.112989]\n",
      "\n",
      "【Height】\n",
      "IQR: 0.139313\n",
      "Lower Bound: 1.421209\n",
      "Upper Bound: 1.978461\n",
      "Outliers Count: 1\n",
      "Outlier Values:\n",
      "[1.98]\n",
      "\n",
      "【Weight】\n",
      "IQR: 42.015907\n",
      "Lower Bound: 2.976140\n",
      "Upper Bound: 171.039767\n",
      "Outliers Count: 1\n",
      "Outlier Values:\n",
      "[173.]\n"
     ]
    }
   ],
   "source": [
    "numeric_cols = ['Age', 'Height', 'Weight']\n",
    "\n",
    "for col in numeric_cols:\n",
    "    Q1 = df[col].quantile(0.25)\n",
    "    Q3 = df[col].quantile(0.75)\n",
    "    IQR = Q3 - Q1\n",
    "    lower_bound = Q1 - 1.5 * IQR\n",
    "    upper_bound = Q3 + 1.5 * IQR\n",
    "    outliers = df[(df[col] < lower_bound) | (df[col] > upper_bound)]\n",
    "\n",
    "    # Print statistical information\n",
    "    print(f\"\\n【{col}】\")\n",
    "    print(f\"IQR: {IQR:.6f}\")\n",
    "    print(f\"Lower Bound: {lower_bound:.6f}\")\n",
    "    print(f\"Upper Bound: {upper_bound:.6f}\")\n",
    "    print(f\"Outliers Count: {len(outliers)}\")\n",
    "    print(\"Outlier Values:\")\n",
    "    print(outliers[col].values)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "590f5827-7810-4cc9-936f-efeb641795db",
   "metadata": {},
   "source": [
    "#### 2.1.3.2 Skewness and Kurtosis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb5bdcac-dc81-4237-bfbb-23efe2dec9a3",
   "metadata": {},
   "source": [
    "To evaluate the distribution characteristics of numerical features, we calculated skewness and kurtosis metrics. \n",
    "\n",
    "The results show that most variables have skewness values close to 0, indicating symmetric distributions. Only Age exhibits moderate skewness (skewness = 1.51), demonstrating a slight right skew that reflects the presence of some older individuals in the sample.\n",
    "\n",
    "Regarding kurtosis, all variables except Age (kurtosis = 2.76) show values near 0, suggesting no significant peak or heavy-tailed distributions. The higher kurtosis of Age indicates a more concentrated distribution with a moderate tail.\n",
    "\n",
    "Based on these skewness and kurtosis results, we conclude that most variables in this dataset exhibit near-normal distribution characteristics. The data quality is sufficiently high that no distributional transformations (e.g., log transformations) are required."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ba70cd3a-f7a0-4a30-b244-b38944b04897",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Age（skew）: 1.5143049022515678\n",
      "Height（skew）: -0.024743014272176542\n",
      "Weight（skew）: 0.2419021286149897\n",
      "\n",
      "Age（kurtosis）: 2.7676901776823444\n",
      "Height（kurtosis）: -0.5558225645820327\n",
      "Weight（kurtosis）: -0.7054782103163917\n"
     ]
    }
   ],
   "source": [
    "print(\"Age（skew）:\", df['Age'].skew())\n",
    "print(\"Height（skew）:\", df['Height'].skew())\n",
    "print(\"Weight（skew）:\", df['Weight'].skew())\n",
    "print()\n",
    "print(\"Age（kurtosis）:\", df['Age'].kurtosis())\n",
    "print(\"Height（kurtosis）:\", df['Height'].kurtosis())\n",
    "print(\"Weight（kurtosis）:\", df['Weight'].kurtosis())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "606e8d4a-5f29-4152-9b79-c3dc47ac5030",
   "metadata": {},
   "source": [
    "### 2.1.4 Dealing with missing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "3f08f1fc-ce2e-4052-ada1-334f42bf7b56",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(0, 17)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>FamilyHistory</th>\n",
       "      <th>HighCalorieFood</th>\n",
       "      <th>VegetablesPerDay</th>\n",
       "      <th>MainMealsPerDay</th>\n",
       "      <th>Snack</th>\n",
       "      <th>Smoke</th>\n",
       "      <th>WaterIntake</th>\n",
       "      <th>CaloriesMonitor</th>\n",
       "      <th>PhysicalActivity</th>\n",
       "      <th>TechUsageTime</th>\n",
       "      <th>Alcohol</th>\n",
       "      <th>TransportMode</th>\n",
       "      <th>ObesityLevel</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Gender, Age, Height, Weight, FamilyHistory, HighCalorieFood, VegetablesPerDay, MainMealsPerDay, Snack, Smoke, WaterIntake, CaloriesMonitor, PhysicalActivity, TechUsageTime, Alcohol, TransportMode, ObesityLevel]\n",
       "Index: []"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(df[df.isna().any(axis=1)].shape) # any(axis=1) Filter out rows with at least one NaN using Boolean index, and return a DataFrame containing these rows\n",
    "df[df.isna().any(axis=1)]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e0b17fb-8561-47cd-af98-a882ad8c2917",
   "metadata": {},
   "source": [
    "## 2.2 Data Transformation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "680ba123-2dda-4e38-a31b-e25acc2f81a5",
   "metadata": {},
   "source": [
    "- Drop Height and Weight columns as they are used in the BMI calculation for our target variable.\n",
    "- Convert categorical variables to category instead of object/text.\n",
    "- Convert synthetic floats & floats to whole integers to better reprsent the ordinal data from orignal survey data."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4783e85d-c18b-4f86-bc5a-b69419af71d2",
   "metadata": {},
   "source": [
    "### 2.2.1 Data binning"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "37583596-8c50-43f9-8134-7995e22bd842",
   "metadata": {},
   "source": [
    "When SMOTE oversampling produced decimal values in our integer-encoded ordinal categorical features, we employed pd.cut() binning to restore them to proper discrete values. The bin boundaries were semantically aligned with the original questionnaire's design to preserve categorical consistency."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e1d401b-f7f0-41a7-b5c7-6f1d6791182b",
   "metadata": {},
   "source": [
    "| Variable              | Value Range            | Category                | Label |\n",
    "|-----------------------|------------------------|--------------------------|-------|\n",
    "| VegetablesPerDay      | 1 ≤ y ≤ 1.5            | Never                   | 0     |\n",
    "|                       | 1.5 < y ≤ 2.5          | Sometimes               | 1     |\n",
    "|                       | 2.5 < y ≤ 3.0          | Always                  | 2     |\n",
    "| MainMealsPerDay       | 1 ≤ y ≤ 1.5            | One                     | 0     |\n",
    "|                       | 1.5 < y ≤ 2.5          | Two                     | 1     |\n",
    "|                       | 2.5 < y ≤ 3.5          | Three                   | 2     |\n",
    "|                       | 3.5 < y ≤ 4.0          | More than three         | 3     |\n",
    "| WaterIntake           | 1 ≤ y ≤ 1.5            | Less than a liter       | 0     |\n",
    "|                       | 1.5 < y ≤ 2.5          | Between 1 and 2 L       | 1     |\n",
    "|                       | 2.5 < y ≤ 3.0          | More than 2 L           | 2     |\n",
    "| PhysicalActivity      | 0 ≤ y ≤ 0.5            | I do not have           | 0     |\n",
    "|                       | 0.5 < y ≤ 1.5          | 1 or 2 days             | 1     |\n",
    "|                       | 1.5 < y ≤ 2.5          | 2 or 4 days             | 2     |\n",
    "|                       | 2.5 < y ≤ 3.0          | 4 or 5 days             | 3     |\n",
    "| TechUsageTime         | 0 ≤ y ≤ 0.5            | 0–2 hours               | 0     |\n",
    "|                       | 0.5 < y ≤ 1.5          | 3–5 hours               | 1     |\n",
    "|                       | 1.5 < y ≤ 2.0          | More than 5 hours       | 2     |\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "2cf9a237-430f-4f1a-a4bc-6d6289bff306",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>FamilyHistory</th>\n",
       "      <th>HighCalorieFood</th>\n",
       "      <th>VegetablesPerDay</th>\n",
       "      <th>MainMealsPerDay</th>\n",
       "      <th>Snack</th>\n",
       "      <th>Smoke</th>\n",
       "      <th>WaterIntake</th>\n",
       "      <th>CaloriesMonitor</th>\n",
       "      <th>PhysicalActivity</th>\n",
       "      <th>TechUsageTime</th>\n",
       "      <th>Alcohol</th>\n",
       "      <th>TransportMode</th>\n",
       "      <th>ObesityLevel</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Female</td>\n",
       "      <td>21.0</td>\n",
       "      <td>1.62</td>\n",
       "      <td>64.0</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>Sometimes</td>\n",
       "      <td>Three</td>\n",
       "      <td>Sometimes</td>\n",
       "      <td>no</td>\n",
       "      <td>Between 1 and 2 L</td>\n",
       "      <td>no</td>\n",
       "      <td>I do not have</td>\n",
       "      <td>3–5 hours</td>\n",
       "      <td>no</td>\n",
       "      <td>Public_Transportation</td>\n",
       "      <td>Normal_Weight</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Female</td>\n",
       "      <td>21.0</td>\n",
       "      <td>1.52</td>\n",
       "      <td>56.0</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>Always</td>\n",
       "      <td>Three</td>\n",
       "      <td>Sometimes</td>\n",
       "      <td>yes</td>\n",
       "      <td>More than 2 L</td>\n",
       "      <td>yes</td>\n",
       "      <td>4 or 5 days</td>\n",
       "      <td>0–2 hours</td>\n",
       "      <td>Sometimes</td>\n",
       "      <td>Public_Transportation</td>\n",
       "      <td>Normal_Weight</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Male</td>\n",
       "      <td>23.0</td>\n",
       "      <td>1.80</td>\n",
       "      <td>77.0</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>Sometimes</td>\n",
       "      <td>Three</td>\n",
       "      <td>Sometimes</td>\n",
       "      <td>no</td>\n",
       "      <td>Between 1 and 2 L</td>\n",
       "      <td>no</td>\n",
       "      <td>2 or 4 days</td>\n",
       "      <td>3–5 hours</td>\n",
       "      <td>Frequently</td>\n",
       "      <td>Public_Transportation</td>\n",
       "      <td>Normal_Weight</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>1.80</td>\n",
       "      <td>87.0</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>Always</td>\n",
       "      <td>Three</td>\n",
       "      <td>Sometimes</td>\n",
       "      <td>no</td>\n",
       "      <td>Between 1 and 2 L</td>\n",
       "      <td>no</td>\n",
       "      <td>2 or 4 days</td>\n",
       "      <td>0–2 hours</td>\n",
       "      <td>Frequently</td>\n",
       "      <td>Walking</td>\n",
       "      <td>Overweight_Level_I</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1.78</td>\n",
       "      <td>89.8</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>Sometimes</td>\n",
       "      <td>One</td>\n",
       "      <td>Sometimes</td>\n",
       "      <td>no</td>\n",
       "      <td>Between 1 and 2 L</td>\n",
       "      <td>no</td>\n",
       "      <td>I do not have</td>\n",
       "      <td>0–2 hours</td>\n",
       "      <td>Sometimes</td>\n",
       "      <td>Public_Transportation</td>\n",
       "      <td>Overweight_Level_II</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Gender   Age  Height  Weight FamilyHistory HighCalorieFood  \\\n",
       "0  Female  21.0    1.62    64.0           yes              no   \n",
       "1  Female  21.0    1.52    56.0           yes              no   \n",
       "2    Male  23.0    1.80    77.0           yes              no   \n",
       "3    Male  27.0    1.80    87.0            no              no   \n",
       "4    Male  22.0    1.78    89.8            no              no   \n",
       "\n",
       "  VegetablesPerDay MainMealsPerDay      Snack Smoke        WaterIntake  \\\n",
       "0        Sometimes           Three  Sometimes    no  Between 1 and 2 L   \n",
       "1           Always           Three  Sometimes   yes      More than 2 L   \n",
       "2        Sometimes           Three  Sometimes    no  Between 1 and 2 L   \n",
       "3           Always           Three  Sometimes    no  Between 1 and 2 L   \n",
       "4        Sometimes             One  Sometimes    no  Between 1 and 2 L   \n",
       "\n",
       "  CaloriesMonitor PhysicalActivity TechUsageTime     Alcohol  \\\n",
       "0              no    I do not have     3–5 hours          no   \n",
       "1             yes      4 or 5 days     0–2 hours   Sometimes   \n",
       "2              no      2 or 4 days     3–5 hours  Frequently   \n",
       "3              no      2 or 4 days     0–2 hours  Frequently   \n",
       "4              no    I do not have     0–2 hours   Sometimes   \n",
       "\n",
       "           TransportMode         ObesityLevel  \n",
       "0  Public_Transportation        Normal_Weight  \n",
       "1  Public_Transportation        Normal_Weight  \n",
       "2  Public_Transportation        Normal_Weight  \n",
       "3                Walking   Overweight_Level_I  \n",
       "4  Public_Transportation  Overweight_Level_II  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# VegetablesPerDay: bins=(1, 1.5, 2.5, 3)\n",
    "veg_bins = [0.999, 1.5, 2.5, 3]\n",
    "df['VegetablesPerDay'] = pd.cut(df['VegetablesPerDay'], bins=veg_bins, labels=['Never', 'Sometimes', 'Always'], right=True, include_lowest=True)\n",
    "\n",
    "# MainMealsPerDay: bins=(1, 1.5, 2.5, 3.5, 4)\n",
    "meal_bins = [0.999, 1.5, 2.5, 3.5, 4]\n",
    "df['MainMealsPerDay'] = pd.cut(df['MainMealsPerDay'], bins=meal_bins, labels=['One', 'Two','Three','More than three'], right=True, include_lowest=True)\n",
    "\n",
    "# WaterIntake: bins=(1, 1.5, 2.5, 3)\n",
    "water_bins = [0.999, 1.5, 2.5, 3]\n",
    "df['WaterIntake'] = pd.cut(df['WaterIntake'], bins=water_bins, labels=['Less than a liter' , 'Between 1 and 2 L' , 'More than 2 L'], right=True, include_lowest=True)\n",
    "\n",
    "# PhysicalActivity: bins=(0, 0.5, 1.5, 2.5, 3)\n",
    "activity_bins = [-0.001, 0.5, 1.5, 2.5, 3]\n",
    "df['PhysicalActivity'] = pd.cut(df['PhysicalActivity'], bins=activity_bins, labels=['I do not have', '1 or 2 days', '2 or 4 days', '4 or 5 days' ], right=True, include_lowest=True)\n",
    "\n",
    "# TechUsageTime: bins=(0, 0.5, 1.5, 2)\n",
    "tech_bins = [-0.001, 0.5, 1.5, 2]\n",
    "df['TechUsageTime'] = pd.cut(df['TechUsageTime'], bins=tech_bins, labels=['0–2 hours', '3–5 hours', 'More than 5 hours'], right=True, include_lowest=True)\n",
    "\n",
    "# Convert object/text variables to category variables\n",
    "columns = [\"Gender\", \"FamilyHistory\", \"HighCalorieFood\", \"Snack\", \"Smoke\", \"CaloriesMonitor\", \"Alcohol\", \"TransportMode\", \"ObesityLevel\"]\n",
    "for col in columns:\n",
    "    df[col] = df[col].astype('category')\n",
    "df.to_csv(\"obesity_data_binning.csv\", index=False)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "953e443e-ba32-42a9-af57-856bd87addab",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2087 entries, 0 to 2086\n",
      "Data columns (total 17 columns):\n",
      " #   Column            Non-Null Count  Dtype   \n",
      "---  ------            --------------  -----   \n",
      " 0   Gender            2087 non-null   category\n",
      " 1   Age               2087 non-null   float64 \n",
      " 2   Height            2087 non-null   float64 \n",
      " 3   Weight            2087 non-null   float64 \n",
      " 4   FamilyHistory     2087 non-null   category\n",
      " 5   HighCalorieFood   2087 non-null   category\n",
      " 6   VegetablesPerDay  2087 non-null   category\n",
      " 7   MainMealsPerDay   2087 non-null   category\n",
      " 8   Snack             2087 non-null   category\n",
      " 9   Smoke             2087 non-null   category\n",
      " 10  WaterIntake       2087 non-null   category\n",
      " 11  CaloriesMonitor   2087 non-null   category\n",
      " 12  PhysicalActivity  2087 non-null   category\n",
      " 13  TechUsageTime     2087 non-null   category\n",
      " 14  Alcohol           2087 non-null   category\n",
      " 15  TransportMode     2087 non-null   category\n",
      " 16  ObesityLevel      2087 non-null   category\n",
      "dtypes: category(14), float64(3)\n",
      "memory usage: 79.9 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c374828a-a5bd-4f7a-af64-22fdefda4b7e",
   "metadata": {},
   "source": [
    "### 2.2.2 Data correlation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f9026fb-9f01-48f7-a0ef-ce72ae05a0d2",
   "metadata": {},
   "source": [
    "#### 2.2.2.1 Visualization of Data Distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "d791e961-95e7-4236-967a-df7d26171f31",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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LEknSsj9OKSYpw8oRAQAAAAAAEn8AAADATbAnJkUH49LkaGejobfWs3Y45eLSyT1qXddB2Xn5mrPuiLXDAQAAAACgxiPxBwAAANwEK3bGSJIGhvjJw8XeytGUn5GhbpKkb3bG6Gh8mpWjAQAAAACgZiPxBwAAAFSwvHxDq6LiJEl3VpPRfgWaejuoX0tf5RvSWz8dtnY4AAAAAADUaCT+AAAAgAq2/cQFJaRlyd3JTt2a+Fg7nHI3pX8zmUzS6qg47Y1JtnY4AAAAAADUWCT+AAAAgAq2al+sJKlvSz852FW/LnhTXzf99b8jGd/48ZCVowEAAAAAoOaqfk8dAAAAgErkymk+w1v7WzmaivNk36aytzXp1yOJ2nAw3trhAAAAAABQI5H4AwAAACpQdZ/ms0Cgl4se6BYkSZq+cr+ycvOsHBEAAAAAADXPdSX+brnlFp0/f77Q/uTkZN1yyy03HBQAAEBNR3+r+iiY5rNfq+o5zeeVnugdrLpujjpxPkP//vW4tcMBAFRT9JMAAACKd11PHk6cOKG8vMJv8GZlZenMmTM3HBQAAEBNR3+rejAMQz/uPydJGhRafaf5LFDL0U7PDWohSZr78xGdSc60ckQAgOqIfhIAAEDx7K6l8nfffWf++scff5SHh4f5c15entavX69GjRqVW3AAAAA1Df2t6uVAbKriUi/J2d5WXRp7Wzucm2JImwB9+scpbTt+Qc+t2KeFYzvIZDJZOywAQDVAPwkAAKB015T4Gzp0qCTJZDJpzJgxFmX29vZq1KiR3nrrrXILDgAAoKahv1W9bDgYL0nq1sRHTva2Vo7m5jCZTJr511Dd8c6v2ngoQV/uiNHw9oHWDgsAUA2Udz/pl19+0RtvvKEdO3YoNjZWK1asMJ9Dujxyf/r06frwww+VlJSkTp06af78+WrVqpW5TlZWlqZMmaLPPvtMmZmZ6t27t959913Vr1/fXCcpKUlPPPGEOXE5ZMgQzZ07V56entd+EwAAAEpxTVN95ufnKz8/Xw0aNFB8fLz5c35+vrKysnTo0CGFh4dXVKwAAADVHv2t6uXn/yb+ejWvY+VIbq4mdWtpct+mkqSXVx5QbApTfgIAblx595MuXryoNm3aaN68eUWWv/7663r77bc1b948RUZGys/PT3379lVaWpq5zqRJk7RixQotX75cmzdvVnp6usLDwy2mIh05cqR2796tNWvWaM2aNdq9e7dGjx59/TcCAACgBNe1xt/x48fl4+NT3rEU6cyZMxo1apS8vb3l4uKiW2+9VTt27DCXG4ahiIgIBQQEyNnZWT179tT+/fst2sjKytLEiRPl4+MjV1dXDRkyRDExMTclfgAAgOtxM/tbqBgXLmZr1+lkSVKvZnWtG4wVPNz9Ft0a6Km0rFz988u9ys83rB0SAKCaKK9+0sCBAzVjxgwNGzasUJlhGJozZ46ee+45DRs2TCEhIVq8eLEyMjK0bNkySVJKSooWLFigt956S3369FFYWJiWLl2qffv2ad26dZKk6OhorVmzRv/+97/VpUsXdenSRR999JG+//57HTp0qNjYsrKylJqaarEBAACUxTVN9Xml9evXa/369eY3rK708ccf33Bg0uWpELp166ZevXpp9erVqlu3ro4dO2YxFULB21eLFi1S06ZNNWPGDPXt21eHDh2Sm5ubpMtvX61cuVLLly+Xt7e3Jk+erPDwcO3YsUO2tjVjyiUAAFD13Iz+FirOpsPxMgypuZ+bAjydrR3OTWdrY9Kb97RR+NxftfloouZvOKqJvYOtHRYAoJqo6H7S8ePHFRcXp379+pn3OTo6qkePHtqyZYvGjRunHTt2KCcnx6JOQECAQkJCtGXLFvXv319bt26Vh4eHOnXqZK7TuXNneXh4aMuWLWrWrFmR5581a5amT59+w9cBAABqnusa8Td9+nT169dP69evV2JiopKSkiy28vLaa68pMDBQCxcuVMeOHdWoUSP17t1bjRs3llR+b18VhTerAACANZVHf+u9995T69at5e7uLnd3d3Xp0kWrV682lzNzQsX6+WCCJOn25jVvtF+BJnVracbQUEnS7HWHteVoopUjAgBUBzfjuVRcXJwkydfX12K/r6+vuSwuLk4ODg6qXbt2iXXq1i3cF6hbt665TlGmTp2qlJQU83b69Okbuh4AAFBzXNeIv/fff1+LFi2q8PnIv/vuO/Xv31/33HOPNm3apHr16mn8+PF65JFHJJXf21dF4c0qAABgTeXR36pfv75effVVNWnSRJK0ePFi3Xnnndq1a5datWrFzAkVKDcvX5sOXV7fryYn/iTp7nb19cef5/Xljhg9sXy3vpvQrUaOgAQAlJ+b9VxKkkwmk8VnwzAK7bva1XWKql9aO46OjnJ0dLzGaAEAAK5zxF92dra6du1a3rEU8ueff+q9995TcHCwfvzxR/3973/XE088oSVLlkgqv7evisKbVQAAwJrKo781ePBg3XHHHWratKmaNm2qV155RbVq1dLvv/9eoTMnQNoTk6zUS7nycLZXWIPapR9Qzb10Z4ia+7kpMT1LDy6KVNqlHGuHBACowm7Gcyk/Pz9JKvTsKD4+3vwcys/PT9nZ2YVGGV5d59y5c4XaT0hIKPQ8CwAAoDxcV+Lv4YcfNj8Qqkj5+flq27atZs6cqbCwMI0bN06PPPKI3nvvPYt65fH21dUcHR3N02IVbAAAADdLefe38vLytHz5cl28eFFdunQpdeYESaXOnFCSmj5t+uYj5yVJtzXxka1Nyf3SmsDZwVb/HtNePrUcdTAuTROW7VJuXn7pBwIAUISb8VwqKChIfn5+Wrt2rXlfdna2Nm3aZE46tmvXTvb29hZ1YmNjFRUVZa7TpUsXpaSkaNu2beY6f/zxh1JSUm7KS/UAAKDmua6pPi9duqQPP/xQ69atU+vWrWVvb29R/vbbb5dLcP7+/mrZsqXFvhYtWujrr7+WZPn2lb+/v7lOcW9fXTnqLz4+ng4WAACotMqrv7Vv3z516dJFly5dUq1atbRixQq1bNnSnLgrauaEkydPSrr+mRMkpk3/7b9r2XVt4m3lSCqP+rVdtGBMe9374VZtOpygad/t14yhIaW+sAcAwNXKq5+Unp6uo0ePmj8fP35cu3fvlpeXlxo0aKBJkyZp5syZCg4OVnBwsGbOnCkXFxeNHDlSkuTh4aGHHnpIkydPlre3t7y8vDRlyhSFhoaqT58+ki4/xxowYIAeeeQRffDBB5KkRx99VOHh4WrWrFl53A4AAAAL15X427t3r2699VZJUlRUlEVZef7j3q1bNx06dMhi3+HDh9WwYUNJlm9fhYWFSfrf21evvfaaJMu3r4YPHy7pf29fvf766+UWKwAAQHkqr/5Ws2bNtHv3biUnJ+vrr7/WmDFjtGnTpmLbKo+ZE6TL06Y/9dRT5s+pqakKDAwsc9xV2cWsXO06fXnKr9ua+Fg5msqlTaCn5twbpsc+3aFP/zilIB9XPdz9FmuHBQCoYsqrn7R9+3b16tXL/Lmg7zJmzBgtWrRITz/9tDIzMzV+/HglJSWpU6dO+umnn8xrIUvS7NmzZWdnp+HDhyszM1O9e/fWokWLLNZC/vTTT/XEE0+YZ1EYMmSI5s2bd83XDQAAUBbXlfjbsGFDecdRpCeffFJdu3bVzJkzNXz4cG3btk0ffvihPvzwQ0mXO3Pl8fYVAABAZVNe/S0HBwc1adJEktS+fXtFRkbq//7v//TMM89IqriZExwdHeXo6Fgu11DVbDtxQTl5hurXdlYDLxdrh1PpDAjx03N3tNCMH6L1yqpo1a/togEhftYOCwBQhZRXP6lnz54yDKPYcpPJpIiICEVERBRbx8nJSXPnztXcuXOLrePl5aWlS5feSKgAAABldl1r/N0sHTp00IoVK/TZZ58pJCREL7/8subMmaP777/fXOfpp5/WpEmTNH78eLVv315nzpwp8u2roUOHavjw4erWrZtcXFy0cuVKi7evAAAAagLDMJSVlVVu69agsC3/neazW2MfprEsxkO3BWlU5wYyDGnS57u0NybZ2iEBAAAAAFAtXNeIv169epX4EOPnn3++7oCuFh4ervDw8GLLy+vtKwAAgMqkPPpbzz77rAYOHKjAwEClpaVp+fLl2rhxo9asWcPMCRVo89HzkqRuwUzzWRyTyaSIwa10+kKmNh1O0EOLt+vbx7upnqeztUMDAFQBN/O5FAAAQFVzXYm/gnnUC+Tk5Gj37t2KiorSmDFjyiMuAACAGq08+lvnzp3T6NGjFRsbKw8PD7Vu3Vpr1qxR3759Janc1q3B/ySmZyk6NlWS1LWxt5WjqdzsbG00b2SY7nl/qw7GpemhRZH6+rGucnW8rn9RAAA1CM+lAAAAindd/1XPnj27yP0RERFKT0+/oYAAAABQPv2tBQsWlFjOzAlld+rUKSUmJpZab/OpTElSQw87nTq8X6dKqR8dHV0O0VVdbk72WjC2g4bO/00H49I09Zt9+r8RtzJFKgCgRDyXAgAAKF65vk47atQodezYUW+++WZ5NgsAAID/or918506dUrNW7RQZkZGqXW9+j8ut1sHat+6r9Tu2X+X+Rw1+SFlPU9nvXt/W4348Hd9t+esOgR5aXTnhtYOCwBQBdFPAgAAKOfE39atW+Xk5FSeTQIAAOAK9LduvsTERGVmZOj+Z96Qb4PGJdb98ay90nOl/gMHKeCuO0ptO3rbJq1e/H+6dOlSeYVbJXVo5KV/DWiuV1ZF6+WVB9Smvoc881PLNMryevn4+KhBgwYV1j4A4OajnwQAAHCdib9hw4ZZfDYMQ7Gxsdq+fbteeOGFcgkMAACgJqO/Vfn4Nmis+sGtii2/mJWr9FPHJUmtWzaTk33p6yCeO3Ws3OKr6h7uHqTIExf004FzmvjpdkXOuEuZ6akVdj5nFxcdjI4m+QcAVRD9JAAAgOJdV+LPw8PD4rONjY2aNWuml156Sf369SuXwAAAAGoy+ltVz5nky+v7+dRyKFPSD5ZMJpNeu6u1dp7apJNJWXIIG6JhXduUOsryepw7dUyfvvZPJSYmkvgDgCqIfhIAAEDxrivxt3DhwvKOAwAAAFegv1X1nEm6nPir7+li5UiqrtquDnrpzhCN/3SnPDrdLUc/Q/WDW1o7LABAJUM/CQAAoHg3tMbfjh07FB0dLZPJpJYtWyosLKy84gIAAIDob1UlBSP+6tV2tnIkVdsdof7qXN9Jv8dc0o4L+WplGDKZTNYOCwBQCdFPAgAAKOy6En/x8fEaMWKENm7cKE9PTxmGoZSUFPXq1UvLly9XnTp1yjtOAACAGoX+VtWSmZ2n8xezJUn1PEn83ahHwty15dh5JctVB+PS1MLf3dohAQAqEfpJAAAAxbO5noMmTpyo1NRU7d+/XxcuXFBSUpKioqKUmpqqJ554orxjBAAAqHHob1UtBaP9vF0d5OzA+n43qrazrVK2filJ2nLsvHLz8q0cEQCgMqGfBAAAULzrGvG3Zs0arVu3Ti1atDDva9mypebPn88iygAAAOWA/lbVUrC+H6P9yk/aju8U0HuM0rNytft0sto38rJ2SACASoJ+EgAAQPGua8Rffn6+7O3tC+23t7dXfj5v4wIAANwo+ltVC+v7lT8jN1utPPMkSZEnkpSZnWfliAAAlQX9JAAAgOJdV+Lv9ttv1z/+8Q+dPXvWvO/MmTN68skn1bt373ILDgAAoKaiv1V1ZOXmKSE9SxIj/spbA5d81anlqOy8fO0+nWztcAAAlQT9JAAAgOJdV+Jv3rx5SktLU6NGjdS4cWM1adJEQUFBSktL09y5c8s7RgAAgBqH/lbVEZtySZLk4WwvV8frmkkfxTCZpA5BtSVJe2KSlZXLqD8AAP0kAACAklzXk4nAwEDt3LlTa9eu1cGDB2UYhlq2bKk+ffqUd3wAAAA1Ev2tquPsf6f5DPB0snIk1VOTOrVU28VeSRk5ijqTqnYNa1s7JACAldFPAgAAKN41jfj7+eef1bJlS6WmpkqS+vbtq4kTJ+qJJ55Qhw4d1KpVK/36668VEigAAEBNQH+r6jmbfHnEX4AH03xWBJPJZE727TyVpNw81m4CgJqKfhIAAEDprinxN2fOHD3yyCNyd3cvVObh4aFx48bp7bffLrfgAAAAahr6W1VLXr6huNT/Jv5Y36/CNPdzVy1HO2Vk5yk6Ns3a4QAArIR+EgAAQOmuKfG3Z88eDRgwoNjyfv36aceOHTccFAAAQE1Ff6tqSUjLUl6+ISd7G9V2sbd2ONWWrY1JbRt4SpJ2n06WYRjWDQgAYBX0kwAAAEp3TYm/c+fOyd6++AcadnZ2SkhIuOGgAAAAair6W1WLeX0/D2eZTCYrR1O9tQxwl72tSRcyshWTlGntcAAAVkA/CQAAoHTXlPirV6+e9u3bV2z53r175e/vf8NBAQAA1FT0t6qWsyn/TfwxzWeFc7SzVXO/y1O77Y1JsXI0AABroJ8EAABQumtK/N1xxx168cUXdenSpUJlmZmZmjZtmsLDw8stOAAAgJqG/lbVYRiGziYXrO/nZOVoaobW9T0kSccS05V2KcfK0QAAbjb6SQAAAKWzu5bKzz//vL755hs1bdpUEyZMULNmzWQymRQdHa358+crLy9Pzz33XEXFCgAAUO3R36o6kjNylJmTJ1sbk+q6kfi7GXxqOaqep7POJGcq6kyqujT2tnZIAICbiH4SAABA6a4p8efr66stW7boscce09SpU2UYhiTJZDKpf//+evfdd+Xr61shgQIAANQE9LeqjjP/nebTz91Jtjas73eztK7vcTnxdzZFHYO8uPcAUIPQTwIAACjdNSX+JKlhw4ZatWqVkpKSdPToURmGoeDgYNWuXbsi4gMAAKhx6G9VDWeTC9b3Y7TfzdS4Ti0529sqIztPJy9c1C0+tawdEgDgJqKfBAAAULJrTvwVqF27tjp06FCesQAAAOAK9LcqN/P6fh7OVo6kZrG1Mam5n5t2nU5W9Nk0En8AUEPRTwIAACiajbUDAAAAAKqai1m5SsnMkST5ezDi72Zr4e8uSfozMV2ZOXlWjgYAAAAAgMqDxB8AAABwjc7+d30/n1oOcrS3tXI0NU8dN0fVdXNUviEdikuzdjgAAAAAAFQaVSrxN2vWLJlMJk2aNMm8zzAMRUREKCAgQM7OzurZs6f2799vcVxWVpYmTpwoHx8fubq6asiQIYqJibnJ0QMAAKC6YJpP6ysY9Rcdm2rlSAAAAAAAqDyqTOIvMjJSH374oVq3bm2x//XXX9fbb7+tefPmKTIyUn5+furbt6/S0v735u+kSZO0YsUKLV++XJs3b1Z6errCw8OVl8e0QAAAALh2Z5Mvj/gL8CTxZy3NfN1kY5Li07KUkJZl7XAAAAAAAKgUqkTiLz09Xffff78++ugj1a5d27zfMAzNmTNHzz33nIYNG6aQkBAtXrxYGRkZWrZsmSQpJSVFCxYs0FtvvaU+ffooLCxMS5cu1b59+7Ru3TprXRIAAECFmzVrljp06CA3NzfVrVtXQ4cO1aFDhyzqMHvCtcvJy1dC+uVEU4An6/tZi7ODrYJ8XCVJ0XGM+gMAAAAAQKoiib/HH39cgwYNUp8+fSz2Hz9+XHFxcerXr595n6Ojo3r06KEtW7ZIknbs2KGcnByLOgEBAQoJCTHXKUpWVpZSU1MtNgAAgKpk06ZNevzxx/X7779r7dq1ys3NVb9+/XTx4kVzHWZPuHZxKZdkGFItRzu5OdlbO5wareV/p/s8GJumvHzDytEAAAAAAGB9dtYOoDTLly/Xzp07FRkZWagsLi5OkuTr62ux39fXVydPnjTXcXBwsBgpWFCn4PiizJo1S9OnT7/R8AEAAKxmzZo1Fp8XLlyounXraseOHfrLX/5SaPYESVq8eLF8fX21bNkyjRs3zjx7wieffGJ+CWvp0qUKDAzUunXr1L9//yLPnZWVpays/02/WJ1eovrfNJ+M9rO2ht6ucnGwVUZ2nk6ev6hb6tSydkgAAAAAAFhVpR7xd/r0af3jH//Q0qVL5eRU/IMVk8lk8dkwjEL7rlZanalTpyolJcW8nT59+tqCBwAAqGRSUlIkSV5eXpIqdvaEWbNmycPDw7wFBgZWxCVZxZkU1verLGxtTGru5yZJOhBbfZLLAAAAAABcr0qd+NuxY4fi4+PVrl072dnZyc7OTps2bdI777wjOzs780i/q0fuxcfHm8v8/PyUnZ2tpKSkYusUxdHRUe7u7hYbAABAVWUYhp566inddtttCgkJkVTy7AkFZdc7e0J1fYkqL99QbPIlSVI9En+VQov/Tvd5PPGiMrJzrRwNAAAAAADWVakTf71799a+ffu0e/du89a+fXvdf//92r17t2655Rb5+flp7dq15mOys7O1adMmde3aVZLUrl072dvbW9SJjY1VVFSUuQ4AAEB1N2HCBO3du1efffZZobKKmD2hur5ElZCWpdx8Q052NvJ2dbB2OJDkU8tRdd0clW9Ih8+lWzscAAAAAACsqlKv8efm5mZ+I72Aq6urvL29zfsnTZqkmTNnKjg4WMHBwZo5c6ZcXFw0cuRISZKHh4ceeughTZ48Wd7e3vLy8tKUKVMUGhpqXqcGAACgOps4caK+++47/fLLL6pfv755v5+fn6TLo/r8/f3N+4ubPeHKUX/x8fE18iWqM8n/m+aztOQobp6W/u6KT0vQgbOpujXQ09rhAAAAAABgNZV6xF9ZPP3005o0aZLGjx+v9u3b68yZM/rpp5/k5uZmrjN79mwNHTpUw4cPV7du3eTi4qKVK1fK1tbWipEDAABULMMwNGHCBH3zzTf6+eefFRQUZFEeFBTE7AnXqCDxxzSflUtTPzfZmkxKSM9SQlqWtcMBAAAAAMBqKvWIv6Js3LjR4rPJZFJERIQiIiKKPcbJyUlz587V3LlzKzY4AACASuTxxx/XsmXL9J///Edubm7mNfk8PDzk7Hx5xBqzJ5SdYUhnrxjxh8rD2d5WQXVcdTQ+XQdiU9XDrY61QwIAAAAAwCqqXOIPAAAAZfPee+9Jknr27Gmxf+HChRo7dqyky7MnZGZmavz48UpKSlKnTp2KnD3Bzs5Ow4cPV2Zmpnr37q1FixbVuNkTUnNMysrNl72tSXXcHK0dDq7S0t9dR+PTdSguTbc18ZGtDVOxAgAAAABqHhJ/AAAA1ZRhGKXWYfaEskvMupxI8vdwJqlUCTX0cpGLg60ysvN04vxFNa5Ty9ohAQAAAABw05H4AwAAAMqgIPEX4Olk5Ugql+jo6ErRpo2NSS383LXjVJIOnE0l8QcAAAAAqJFI/AEAAABlkJhlI0mqx/p+kqTUCwmSpFGjRlXYOdLT06+pfgt/N+04laQT5y8qIztXLg78uwMAAAAAqFn4TxgAAAAohZ2nny7lmWRrMsnPnRF/kpSZnipJGjTuOTVr3a5c247etkmrF/+fLl26dE3HeddylK+7o86lZulgXJraNqhdrnEBAAAAAFDZkfgDAAAASuEYGCJJquvuKDtbGytHU7l4BzRU/eBW5drmuVPHrvvYlv7uOpeaoAOxqQoL9JTJxHqMAAAAAICag6cWAAAAQCmcAi8ntpjms/Jr6usmWxuTzqdnKyEty9rhAAAAAABwU5H4AwAAAErhWJ/EX1XhZG+rxj6ukqQDsalWjgYAUJVFRETIZDJZbH5+fuZywzAUERGhgIAAOTs7q2fPntq/f79FG1lZWZo4caJ8fHzk6uqqIUOGKCYm5mZfCgAAqEFI/AEAAAAluJCZJ/vaAZIM+Xuyvl9V0DLAXZJ0KC5Nufn5Vo4GAFCVtWrVSrGxseZt37595rLXX39db7/9tubNm6fIyEj5+fmpb9++SktLM9eZNGmSVqxYoeXLl2vz5s1KT09XeHi48vLyrHE5AACgBiDxBwAAAJTgQEK2JMnT3pCjna2Vo0FZBHq5qJajnS7l5ut4wkVrhwMAqMLs7Ozk5+dn3urUqSPp8mi/OXPm6LnnntOwYcMUEhKixYsXKyMjQ8uWLZMkpaSkaMGCBXrrrbfUp08fhYWFaenSpdq3b5/WrVtnzcsCAADVGIk/AAAAoAQFiT8fJ8PKkaCsbEwmtfS/POpvT0yKlaMBAFRlR44cUUBAgIKCgjRixAj9+eefkqTjx48rLi5O/fr1M9d1dHRUjx49tGXLFknSjh07lJOTY1EnICBAISEh5jrFycrKUmpqqsUGAABQFiT+AAAAgBIcSPxv4s+RKSOrkpB67jKZpDPJmUpMz7J2OACAKqhTp05asmSJfvzxR3300UeKi4tT165ddf78ecXFxUmSfH19LY7x9fU1l8XFxcnBwUG1a9cutk5xZs2aJQ8PD/MWGBhYjlcGAACqMxJ/AAAAQDGSM7J1KiVXkuTtyIi/qsTNyV6N69SSJO05nWzdYAAAVdLAgQN11113KTQ0VH369NEPP/wgSVq8eLG5jslksjjGMIxC+65WljpTp05VSkqKeTt9+vR1XgUAAKhpSPwBAAAAxdh2/IIkKef8aTmxvF+V06a+hyTpYFyaLuXkWTkaAEBV5+rqqtDQUB05ckR+fn6SVGjkXnx8vHkUoJ+fn7Kzs5WUlFRsneI4OjrK3d3dYgMAACgLEn8AAABAMbYcOy9JunRyr5UjwfWo5+ks71oOys03dCCWtZEAADcmKytL0dHR8vf3V1BQkPz8/LR27VpzeXZ2tjZt2qSuXbtKktq1ayd7e3uLOrGxsYqKijLXAQAAKG8k/gAAAIBi/HY0UZJ06eQeK0eC62EymdSmvqckadepZOXlM10rAKDspkyZok2bNun48eP6448/dPfddys1NVVjxoyRyWTSpEmTNHPmTK1YsUJRUVEaO3asXFxcNHLkSEmSh4eHHnroIU2ePFnr16/Xrl27NGrUKPPUoQAAABXBztoBAAAAAJVRfOolHYlPl0nSpVOM+KuqWvi56fc/zys9K1fRsakKqedh7ZAAAFVETEyM7rvvPiUmJqpOnTrq3Lmzfv/9dzVs2FCS9PTTTyszM1Pjx49XUlKSOnXqpJ9++klubm7mNmbPni07OzsNHz5cmZmZ6t27txYtWiRbW+YQBwAAFYPEHwAAAFCE345dHu13S217nbiUbuVocL3sbG3UrmFt/XokUZEnLqiFP2skAQDKZvny5SWWm0wmRUREKCIiotg6Tk5Omjt3rubOnVvO0QEAABSNqT4BAACAIvx29PL6fqF1HawcCW5UaD0POdvbKvVSrg6dS7N2OAAAAAAAVBgSfwAAAMBVDMPQlv+u79fa19HK0eBG2dvaqG1DT0lS5PELYqk/AAAAAEB1ReIPAAAAuMrxxIs6m3JJDrY2auHDiL/qoHU9TznZ2yg5M0fH0vg3CAAAAABQPfEfLwAAAHCV345dnuazbUNPOdqZrBwNyoODnY26NvaRJB1IsZWta20rRwQAAAAAQPkj8QcAAABcZfORBElSt/8milA9tApwl6+7o3INkzx7PmDtcAAAAAAAKHck/gAAAIArZOfm67ejl0f89WhWx8rRoDzZmEzq2ayuJEO1Qm7XvnNZ1g4JAAAAAIByReIPAAAAuML2kxeUnpUrn1oOCgnwsHY4KGd+7k4KqpUvSXr792TFpVyyckQAAAAAAJQfEn8AAADAFTYeujzNZ4+mdWVjw/p+1VFrzzxlxx9XSla+Hvt0h7Jy86wdEgAAAAAA5YLEHwAAAHCFDQfjJUm9mjPNZ3VlZyMlrHhFrvYm7TqVrKlf71NuXr61wwIAAAAA4IbZWTuAksyaNUvffPONDh48KGdnZ3Xt2lWvvfaamjVrZq5jGIamT5+uDz/8UElJSerUqZPmz5+vVq1ametkZWVpypQp+uyzz5SZmanevXvr3XffVf369a1xWQAAAKikTl/I0JH4dNnamNS9CYm/6iw3OU6TOntq1uYkfbPrjJIysjVvZFu5Olbqf5EAAACACtXun0usHQJQ5ex442/WDsFCpR7xt2nTJj3++OP6/ffftXbtWuXm5qpfv366ePGiuc7rr7+ut99+W/PmzVNkZKT8/PzUt29fpaWlmetMmjRJK1as0PLly7V582alp6crPDxceXlM6QMAAKq3X375RYMHD1ZAQIBMJpO+/fZbi3LDMBQREaGAgAA5OzurZ8+e2r9/v0WdrKwsTZw4UT4+PnJ1ddWQIUMUExNzE6/i5tl4+PI0n20beMrDxd7K0aCitfN30vuj2snJ3kYbDiXonve3avuJC9fUhmEYunAxW7tPJ2vVvlh9sf20lmw9oWV/nNKqfbHadvyCUjJzKugKAAAAAACwVKlfZ12zZo3F54ULF6pu3brasWOH/vKXv8gwDM2ZM0fPPfechg0bJklavHixfH19tWzZMo0bN04pKSlasGCBPvnkE/Xp00eStHTpUgUGBmrdunXq37//Tb8uAACAm+XixYtq06aNHnjgAd11112Fygteolq0aJGaNm2qGTNmqG/fvjp06JDc3NwkXX6JauXKlVq+fLm8vb01efJkhYeHa8eOHbK1tb3Zl1ShNh26PM1nz2Z1rRwJbpZ+rfy07JHOenjxdh2ITdXd729V7+Z1NeTWAHUK8pafh5Okywm+pIwcxSRl6GBcmg7GpulgXKoOxqXpwsXsUs9Tv7azujb2Vp8Wvrot2EcuDpX6XzEAAAAAQBVVpf7bTElJkSR5eXlJko4fP664uDj169fPXMfR0VE9evTQli1bNG7cOO3YsUM5OTkWdQICAhQSEqItW7YUm/jLyspSVlaW+XNqampFXBIAAECFGjhwoAYOHFhkGS9RWbqUk6ffjp6XJPUi8VejtG1QW6v/0V1z1h3WF9tjtP5gvNb/d61HB1sbOdjZKCcvX1m5xa8D6OfupPq1neXmZCcne1vl5BlKyczW2eRLOpOcqZikTH2xPUZfbI+Rq4Ot7gyrp5EdGyiknsfNukwAAAAAQA1QZRJ/hmHoqaee0m233aaQkBBJUlxcnCTJ19fXoq6vr69OnjxpruPg4KDatWsXqlNwfFFmzZql6dOnl+clAAAAVCq8RGVp0+EEZebkqZ6ns1r4u1k7HNxkvu5OmjWstR667RZ9tu2U/jh+XgfOpio7L1/Zef9L+PnUclRT31pq7ueu5v5uauHnrmDfWnKyL370a0pGjvbEJOvng/FaF31OMUmZWvbHKS3745S6B/toUp9gtWvodTMuEwAAAABQzVWZxN+ECRO0d+9ebd68uVCZyWSy+GwYRqF9VyutztSpU/XUU0+ZP6empiowMPAaowYAAKi8eInK0up9sZKkASF+pfYlUX01qVtLL4S3lCRdzMpVcmaOcnLzZWtjUl13RznaXfv0th4u9vpL0zr6S9M6mja4pX7/84KWbTul1fti9euRRP16JJEEIAAAAACgXNhYO4CymDhxor777jtt2LBB9evXN+/38/OTpEIPneLj480PsPz8/JSdna2kpKRi6xTF0dFR7u7uFhsAAEB1VFEvUaWkpJi306dPl0usFSUrN0/roy9P7TgwxM/K0aCycHW0Uz1PZzXycVWgl8t1Jf2uZjKZ1KWxt+beF6YNU3pqRIdA2dmY9OuRRN313lb97eNt2n82pRyiBwAAAADURJU68WcYhiZMmKBvvvlGP//8s4KCgizKg4KC5Ofnp7Vr15r3ZWdna9OmTerataskqV27drK3t7eoExsbq6ioKHMdAACAmoiXqP5ny9HzSsvKVV03R7VtULv0A4ByEOjlolfvam2RAPzlcILC527W5C/2KDYl09ohAgAAAACqmEo91efjjz+uZcuW6T//+Y/c3NzMD6U8PDzk7Owsk8mkSZMmaebMmQoODlZwcLBmzpwpFxcXjRw50lz3oYce0uTJk+Xt7S0vLy9NmTJFoaGh6tOnjzUvDwAAwKqufIkqLCxM0v9eonrttdckWb5ENXz4cEn/e4nq9ddft1rs5W111P+m+bSxYZpP3JhTp04pMTHxmo4ZHiT9pY6Plu1L0+bTl/T1zhit3BOjwU1d9dfmteRi/793Nn18fNSgQYPyDhsAAAAAUA1U6sTfe++9J0nq2bOnxf6FCxdq7NixkqSnn35amZmZGj9+vJKSktSpUyf99NNPcnNzM9efPXu27OzsNHz4cGVmZqp3795atGiRbG1vfKoeAACAyiw9PV1Hjx41fz5+/Lh2794tLy8vNWjQgJeoJOXk5eunA+ckXU78oeaIjo4u9zZjY2N19z336FLm9Y/Wc/Bvqtq9HpQCQ/R19EV9sf2Mkn9bpvQ9P0r5eXJ0ctLXX30lf3//coz8MpKKAAAAAFC1VerEn2EYpdYxmUyKiIhQREREsXWcnJw0d+5czZ07txyjAwAAqPy2b9+uXr16mT8/9dRTkqQxY8Zo0aJFvEQl6Y8/Lyg5I0derg7q2MjL2uHgJki9kCBJGjVqVIWd469PvKSgZiHXfbxhSLGZOdqXbKd0V0959xuvhnc8pjqpB7Vx7j8VHh5ejtH+j7OLiw5GR5P8AwAAAIAqqlIn/gAAAHBjevbsWeLLVLxEJX2zK0aS1L+Vn+xsK/US2CgnmempkqRB455Ts9btyrXt6G2btHrx/8nN20/1g1vdUFuBktrlG4o6m6I//ryg9Jw8pbu0kO/IV9XS01BYq2blE/R/nTt1TJ++9k8lJiaS+AMAAACAKorEHwAAAGqs9Kxcrd53eR3pu9vVt3I0uNm8AxrecHLuaudOHSvX9mxtTGpT31PN/dy042SSdpw4L6fAEP0pyS6rlro29pGHs325nhMAAAAAUHXxSjMAAABqrFX7YpWZk6dbfFzVtoGntcMBiuVoZ6uujX3UTseUvm+dJEOHz6Xrk60n9cuRBF3KybN2iAAAAACASoDEHwAAAGqsr3dcnubzrnb1ZTKZrBwNUDpH5er8qjlq55SgQC9n5RmGdp1K1qItJ7TzVJJy8/OtHSIAAAAAwIpI/AEAAKBGOn0hQ38cvyCTSfprWD1rhwNck1o2ufrrrfV0560B8nZ1UFZuvn49kqhPtp7U4XNpJa7tCQAAAACovljjDwAAADXS1zsvj/a7rYmPAjydrRwNcO1MJpMaebuqgZeLDsSm6vdj55V6KVero+K0091R3ZvUUb3a/GwDAAAAQE3CiD8AAADUODl5+fps2ylJ0t3t6ls5GuDG2JhMCgnw0JiujdT5Fi/Z25p0LjVLX+2M0fd7zyopI9vaIQIAAAAAbhJG/AEAAKDGWbUvVudSs1THzVEDQ/ytHQ5QLuxtbdQpyFshAR76/fh57T+TqmMJF3U88aJC6nmoU5CXXBz4FxAAAAAAqjP+6wMAAECNYhiGFmw+Lkn6W+eGcrBjEgxUL66Odurd3Fe31vfUb8fO63jiRe2NSdHB2DTdGuipWwM95exge9PjOnXqlBITE2UYhi5k5isuPVfxGXlKyszXpVxDWXmG7GwkB1uTajnYyMfFVnVcbFXf3U4OtqZS2/fx8VGDBg1uwpUAAAAAQOVF4g8AAAA1yvaTSdobkyIHOxuN7ESSANWXdy1HDWkToJikDP16JFHxaVnaduKCdp5KUkiAh9o29JSbk32Fx5GVm6cftx/Wg/+aJVvfYDnUvUW2rp5lPt7Iz1POhRhlnTmorNNRunRyr/LSzxeq5+ziooPR0ST/AAAAANRoJP4AAABQoyz49fJov2Fh9eRdy9HK0QAVr35tF43oEKijCenafiJJ8WlZ2h2TrL1nktXMz02t63nK1718fxdOX8jQxsMJ2nQoXluOnVdGdp5qdb7XXG6SIRc7ycXWkLOdITuTZGeS8iXlGVJ2nkkZedLFXJOyZSsHn4Zy8Gkotzb9JUleDvkKcMlXPed81bKXzp06pk9f+6cSExNJ/AEAAACo0Uj8AQAAoMY4Gp+unw7ESZIevC3IytEAN4/JZFJwXTc1qVNLpy5kaPvJJMUkZSo6Nk3RsWnyruUgfzsb2XnVu672z6dnadvxC/r9z/P67dh5HY1Ptyj3cLTR2d0b1a1rVzVrEiQfVwfZ2ZY+za5hGLqYlaf4tEs6m3xJMckZOpeapQvZNrqQbaOoZMmnloP8PIJl61r7umIHAAAAgOqExB8AAABqjLd+OqR8Q+rb0ldNfd2sHQ5w05lMJjX0dlVDb1fFpVzSnphkHYlP1/n0bJ2Xneo98oHGr4pX1yO7dWsDTzXydlW92s5yc7STva2NcvLzlXQxRwlpWTqWkK7D59K0/USSDp1LsziPrY1JbRt4qmezuurRtI4uxR1Vh5deV+P+38jP3ema4q3lZKdaTrV0S51akqSLWbk6lpCuownpiknKVGJ6thJlp3rjF+nV3y7oMZd4/aVpHdnalL4uIAAAAABUNyT+AAAAUCPsOZ2s1VFxMpmkf/ZvZu1wAKvz83CSn4efejTN05Fz6Yo6GadzF/MVly59s+uMvtl15praa+7nps63eKtTkJe6NvGRh/P/1g/cea78knCujnZqXd9Tret76lJOno4mpGvXsThdyLbVtjNZ2rYoUv/f3p3Hx3Tvfxx/zWQZ2RNbiC22hFgTiRatUrRVXbRarVqLKhJqbaku7rUL11LkWkOV0ltLW1WkainqipBaS2qLIIkQJJFFkvn9QeY21f7aKpks7+fD9zEz33Pmez4n38cc35nPOd9TxsFI+1pOtKvhiIvpj68s/CNly5bVFKIiIiIiIlIkKPEnIiIiIiVC6OYTALzoX1lX+4n8Qik7GxpUdsMjPY4ZQ7uxYG0EN+zLcvTCdeKS07lwLZ20rGzMZjAYwN3BjtJO9lQv60yt8s40rOzGQ9VLW+WemaXsbKjv5Ybx/EGWhU3AueETONV/nCu48snhFD6Oukzq4W9J2f8F2dcu3fN2HBwd+en4cSX/RERERESk0FPiT0RERESKvZ0nL7Pr5yTsbAwMaVvb2uGIFFrmrJsEVCxFQIDPXctycs0YAGMhnEIzPfUGt5JiaV7bk1o+pYhLyyYmxch1SuHa5Blcm3TAy8FMbZccypjMGP7CLiTEnmLFlJEkJSUp8SciIiIiIoWeEn8iIiIiUqzdzMpmzPrDAHR/2JsqpR2tHJFI0VQU7plXxqsa1XzqUQ1objYTl5zOgdhkzl65ycV0AxfTjZR3MRFQ1YPa5Z0LZRJTRERERETk71DiT0RERESKtWmbT3L+ajpebqUY9sTdVzGJSPFkMBioUtqRKqUduZqWxcHzyRy/lEJiSiabjsaz+5Qt/lXcqeflhr3t378PoIiIiIiISGGgxJ+IiIiIFFtR55IJ33MGgIkvNsDZpOGvSElU2smeNnU8aV6jLIfirvFj3HVSMrLZGZPEf89cpUElNxpXccdJxwgRERERESni9K1GRERERIqlazezGLo6GrMZXgyoRCvf8tYOSaRIOH78eJFo81442NvwUI0yNKnmwfFLKRyITeZa+i32n0vmYOw1fCu4EFDVnTLOJmuHKiIiIiIick+U+BMRERGRYic7J5eQlQeJvXqTyh4OfPCMn7VDEin0bly9DEC3bt0e2DZSU1MfWNt/ha2NkQaV3ahfyZXTSWlEnUvm0vUMjl26wbFLN/Au44h/VQ+qeDhYO1QREREREZG/RIk/ERERESl2Jmw8zq6fk3C0t2Fhj0DcHe2tHZJIoZeeegOADm+Owbdhk/va9vF9O/hm2SwyMjLua7t/l8FgoGY5Z2qWc+bS9XSiziVz6nIaZ6/c5OyVm7g52FHZ3oiNk4e1QxUREREREflTlPgTERERkSIvNjaWpKQkzGYzK4+ksOZ4GgAhgS6kX/qZA5fuve3CMkWhSEEp41WNyrXr3dc2E2JP3df2HoSKbg4809CB5JtZRMde46f4FK6n3+J6ui2VBi5l8u6r9LK7RCvfcjja66u0iIiIiIgUTvq2IiIiIiJFWmxsLHXq1iX95k08Hu+La1BHAK5+t4jgKevv23YKyxSFIvJgeTja07pOeR6pXZaTCSkcOBXP1Swb9l3IZN+KA5hsjbT0KcdT9SrQtq4nbo521g5ZRERERETEQok/ERERESnSkpKSyMwxEPDOSq7gCkBjj2xq9uoBvXr87fYL6xSFIvJg2dkYqeflhltaHB99OIQBU5dx8LKZ2Ks3iTiWQMSxBIwGqOflRpB3aZpWL02QtwdlnE3WDl1EREREREowJf5EREREpEg7kZRFxddncwVXjAZoU8cTPy/X+9Z+UZiiUEQerFtJsfRs5MpMf3+OX0ph09F4thyN56f4FA5fuM7hC9dZsvsMAN5lHPGt4IKvpws+dx6rlnHEZGtj5b0QEREREZGSQIk/ERERESmyrt3M4h87r2Lr5omTrZln/avi6VrK2mGJSDFlMBjw83LFz8uVYe18uHQ9nX1nrhJ59iqRZ5I5kZDC2Ss3OXvlJpuPJuR7b3kXE5U9HKjk4Xj70d2BSh4OVHQrRXmXUrg72GE0Gqy0ZyIiIiIiUlwo8SciIiIiRZa7oz3dGrgwa8WXPPdEcyX9ROSBOX78+G/WVwGqVIUXqzqTkunImWu3iL2eTez1W5y7ns35G9lkZJtJTMkkMSWTA7HXfrMdW6OBci4myrmYKH/nsZxLKcvr/9WZdPWgiIiIiIj8rhKV+Js3bx6hoaFcunSJevXqMXPmTB599FFrhyUiIiJSJBTWsVT7Wo68/9U07J5aa+1QRKQYunH1MgDdunW75zaMDq7YupXH1s0TG9fy2LqWw9bNE1u38tg4l8bG0Y3sXDOXrmdw6fof30/U2d6Am8mIq8mIi8mIq/0vnpuMuNj/77FKhbL41qymZOGvxMbGkpSUBIDZbCY7F7JyzNzKNd9+zIEsy3MzRoMBW+PtBO2vH002BkrZGrD5xRWbZcuWpWrVqtbaPSmkCutYSkRERIqXEpP4W716NUOGDGHevHm0aNGC+fPn0759e44dO6bBuIiIiMgfKMxjKYNBU+OJyIOTnnoDgA5vjsG3YZP73v7pI/tZHzoVGyc3bJxKY+PsgY2Tx+2EoFNpbJzc7zy/XW+wtSM1y0xqVg4XUnL+xBYuA8extzHiXMoWZ9Pt4lLqdnE22eJgb4O9jRGTnQ0mW+OdYoN93nM7I7ZGIwYDGLh9zL39HMvzO88AMzm5kGs2k2s2YzbnPefO6/8tN/+iPtfMndd/fnl2Tt5rM9m5ZnLzHu/UZWXnkpmdQ2Z27p2SQ+atXFIzMom7mAA2dhhs7TDY2mMwGP92X5qzs8jNSsd8KxOyM2lQ1wd3Z0ecTDY42NviZG+Do70tjvY2OJpscLSzwdF0+7VTXr297e1lv1jXzubvx/an4r/TX4CmnX0ACvNYSkRERIqXEpP4+9e//kWfPn3o27cvADNnzmTz5s2EhYUxadKku9bPzMwkMzPT8vr69esA3Lhx44HHmpOZ/sC3IVLcFMRns6DoGCDy1xXEMSBvG+a8X8RKmMI8lkpNTQUgLuYomek373v7CbGnAIg/e5JTTo5quwDaV+zWab+otv2g289r+1ZW5gM5xqReuwq52QQ80pbK1Wv/zlrJQDLmdMjBhiyDHTnYcgsj2QZbsrG982hDtsGGbGy5ZbAl22zEbLz9tT8DyLgJSfd9D4omg92dqaFzczFnZfDL/90N5lyM5GLEjIFcDHeWmjHcKb9cYvhl5hODjR0GGzvAmaPnrwBX/nastkYw2Row3tnO7fTqHzObzZiB3Dsr306eghkzd/7def2/dQCe9PVg+mtN/3bcv6axVOEdS/2avpOK/HX6XUqkZCt0v0uZS4DMzEyzjY2Nee3atfnqBw8ebG7ZsuVvvufDDz80c2ccrKKioqKioqKSV86fP18Qw5dCRWMpFRUVFRUVlftVNJb6H42lVFRUVFRUVP5q+TNjqRJxxV9SUhI5OTl4enrmq/f09CQ+Pv433zN69GiGDRtmeZ2bm8vVq1cpU6aMppMqoW7cuEGVKlU4f/48rq6u1g5HRAqYjgFiNptJSUnBy8vL2qEUOI2lChcdjwoX9Ufhob4oXNQfhUth6A+NpTSWkr+nMHyORcR6dAyQvzKWKhGJvzy/HhiZzebfHSyZTCZMJlO+Ond39wcVmhQhrq6uOriKlGA6BpRsbm5u1g7BqjSWKlx0PCpc1B+Fh/qicFF/FC7W7g+NpTSWkr/P2p9jEbEuHQNKtj87liqYO0RbWdmyZbGxsbnrLKrExMS7zrYSERERkfw0lhIRERG5dxpLiYiISEEqEYk/e3t7mjRpQkRERL76iIgImjdvbqWoRERERIoGjaVERERE7p3GUiIiIlKQSsxUn8OGDaN79+4EBgbSrFkzFixYQGxsLP3797d2aFJEmEwmPvzww7um2hCRkkHHACnpNJYqPHQ8KlzUH4WH+qJwUX8ULuoP69NYSv4ufY5FSjYdA+SvMJjNZrO1gygo8+bNY+rUqVy6dIn69eszY8YMWrZsae2wRERERIoEjaVERERE7p3GUiIiIlIQSlTiT0RERERERERERERERKS4KhH3+BMREREREREREREREREp7pT4ExERERERERERERERESkGlPgTERERERERERERERERKQaU+BMREREREREREREREREpBpT4ExEREREpZCZNmkRQUBAuLi6UL1+ejh07cuLEiXzrmM1mxo4di5eXFw4ODrRq1YqjR49aKeLiLSwsjIYNG+Lq6oqrqyvNmjXjm2++sSxXX1jPpEmTMBgMDBkyxFKn/ig4Y8eOxWAw5CsVKlSwLFdfFLwLFy7QrVs3ypQpg6OjI40bNyYqKsqyXH0iIiIiUvwp8SciIiIiUsjs2LGD4OBg9u7dS0REBNnZ2TzxxBOkpaVZ1pk6dSr/+te/mDNnDpGRkVSoUIF27dqRkpJixciLp8qVKzN58mT279/P/v37efzxx3n++ectP5arL6wjMjKSBQsW0LBhw3z16o+CVa9ePS5dumQphw8ftixTXxSs5ORkWrRogZ2dHd988w3Hjh1j+vTpuLu7W9ZRn4iIiIgUfwaz2Wy2dhAiIiIiIvL7Ll++TPny5dmxYwctW7bEbDbj5eXFkCFDeOeddwDIzMzE09OTKVOm8Oabb1o54uKvdOnShIaG0rt3b/WFFaSmphIQEMC8efMYP348jRs3ZubMmfpsFLCxY8eyfv16oqOj71qmvih4o0aNYvfu3Xz//fe/uVx9IiIiIlIy6Io/kftMuXQRAR0LROT+un79OnA72QRw5swZ4uPjeeKJJyzrmEwmHnvsMfbs2WOVGEuKnJwcVq1aRVpaGs2aNVNfWElwcDAdOnSgbdu2+erVHwUvJiYGLy8vqlevzquvvsrp06cB9YU1fPnllwQGBvLyyy9Tvnx5/P39WbhwoWW5+kSkeFmxYgVr1qyxdhgiIlII2Vo7AJGiymw2YzAYiIqK4uTJk1y+fJlOnTpRqVIla4cmIlaWd3zYvn0733//PUePHqVnz574+flRrVo1a4cnIkWM2Wxm2LBhPPLII9SvXx+A+Ph4ADw9PfOt6+npyblz5wo8xpLg8OHDNGvWjIyMDJydnVm3bh1+fn6WH8vVFwVn1apVHDhwgMjIyLuW6bNRsB566CE+/vhjfHx8SEhIYPz48TRv3pyjR4+qL6zg9OnThIWFMWzYMN5991327dvH4MGDMZlM9OjRQ30iUoy8/fbbfP755wQHB5OQkHDX51pEiqfz589jNpvJysqiVq1alvq836FE8ijxJ3KPDAYDa9asYfDgwVSvXh2j0cjo0aNZuHAhL730Evb29tYOUUSsxGAwsHbtWnr37s3zzz+Pg4MDffv2pU2bNoSGhupLmYj8JSEhIRw6dIhdu3bdtezXX+70he/B8fX1JTo6mmvXrrFmzRp69uzJjh07LMvVFwXj/PnzvPXWW2zZsoVSpUr97nrqj4LRvn17y/MGDRrQrFkzatasybJly3j44YcB9UVBys3NJTAwkIkTJwLg7+/P0aNHCQsLo0ePHpb11CciRdv06dMJDw/n66+/pmnTptYOR0QKyNq1axk9ejS3bt3i6tWrdO/enUGDBuHj46P/x+UumupT5B4dPHiQAQMGMG7cOHbt2sW6detIT08nNjZWST+REipves/Tp08zZswYpk2bxrJly1i0aBFJSUlUq1ZNST8R+UsGDRrEl19+ybZt26hcubKlvkKFCsD/rm7Kk5iYqOPMA2Jvb0+tWrUIDAxk0qRJNGrUiFmzZqkvClhUVBSJiYk0adIEW1tbbG1t2bFjB7Nnz8bW1tbyN1d/WIeTkxMNGjQgJiZGnw0rqFixIn5+fvnq6tatS2xsLKD/O0SKg7S0NHbu3Ml7771H06ZNOXXqFGvWrOHpp5+mZ8+exMXFWTtEEXkAduzYQbdu3Rg6dChLliwhPDycVatWMWbMGMs06yK/pMSfyJ/w3//+9666ixcv0rx5c3r37k1MTAz+/v688cYbjBo1CoD09HRA9/kSKe7Wr1/P3r17gf+dPZ2VlYWTkxN9+vThxIkTeHt706NHD8aNGwfcni4uIyPDajGLSOFnNpsJCQlh7dq1fPfdd1SvXj3f8urVq1OhQgUiIiIsdVlZWezYsYPmzZsXdLglktlsJjMzU31RwNq0acPhw4eJjo62lMDAQLp27Up0dDQ1atRQf1hRZmYmx48fp2LFivpsWEGLFi04ceJEvrqTJ09apppXn4gUfU5OTtjZ2bFq1So+/fRTBgwYwLx58/Dy8mLnzp0MGDDA2iGKyAOwZcsWWrduTf/+/WnVqhUvvPACERERbN26lblz51o7PCmElPgT+QNRUVE0a9aMqVOn5qv/6aefuHjxIhcuXKBdu3a0b9+esLAw4Pal10OGDCEzM1OXWosUU2azmQsXLvD6668zbdo0oqKiLMvi4uK4fPkyMTExPP3007Rv35758+cDt08kmDlzps7EFJH/V3BwMJ988gkrV67ExcWF+Ph44uPjLScWGQwGhgwZwsSJE1m3bh1HjhyhV69eODo68tprr1k5+uLn3Xff5fvvv+fs2bMcPnyYMWPGsH37drp27aq+KGAuLi7Ur18/X3FycqJMmTLUr19f/VHARowYwY4dOzhz5gz//e9/eemll7hx4wY9e/ZUX1jB0KFD2bt3LxMnTuTnn39m5cqVLFiwgODgYED/d4gUZTk5OZbn/fv3x83NjQEDBtC8eXMmTJjAokWLGDFiBEajkaysLCtGKiL3m9lsJj4+nuzsbOD21N5ZWVk0btyY2bNn88knn+g3JrmL7vEn8geaNGnCzJkzGTlyJEajkeHDh2MwGHjqqadYu3YtderU4eWXX2b+/Pnk5uYCsHfvXi5dukRGRgYmk8nKeyAiD4LBYKBSpUps2LCB3r17M3XqVEaOHElgYCBt27bF29ubOnXq0LNnTxYsWGB537p16zhx4gSurq5WjF5ECru8k4latWqVrz48PJxevXoB8Pbbb5Oens7AgQNJTk7moYceYsuWLbi4uBRwtMVfQkIC3bt359KlS7i5udGwYUM2bdpEu3btAPVFYaP+KDhxcXF06dKFpKQkypUrx8MPP8zevXstV5ipLwpWUFAQ69atY/To0fzzn/+kevXqzJw5k65du1rWUZ+IFD1hYWHs2rWLzMxMGjZsyAcffEDbtm25ePEiXl5elvXWrVuHt7e3bj8jUkxcvXqVUqVK4ejoyLPPPsurr77Kt99+S9u2bbG1vZ3WcXZ2pkyZMjg6Olo5WilsDGbNQyjyp8ydO5dBgwYxZcoURo4cyc2bN3nnnXf4+uuv6dmzJ++++y6xsbEsXryY+fPns3PnTurVq2ftsEXkAcrNzcVoNLJnzx66d+9OYGAgI0aMICgoiK+++op//vOfmEwmFi1aRFxcHJs3b2b+/Pns2rWLhg0bWjt8ERERERERKcTeeecdli1bRkhICA4ODrz77rt06NCBtWvXArfv+bd3716mTJlCfHw8Bw4cwNbWFrPZrBmoRIqw9evXM23aNBISEnjttddo1qwZmzZtYsuWLcyaNctyAuLo0aOJiIggIiICDw8PK0cthYmu+BP5k/KmRxk0aBA5OTmMGjWKcePGkZuby2effcbUqVOpW7cuqampfPvtt0r6iZQARqOR3NxcmjdvzvLly+nevTuhoaG8//77PPPMM9jY2DB58mSCgoKoUqUKpUuX5vvvv1fST0RERERERP5fkZGRfPnll3z++ec88sgjfPHFF9jb21t+8Ac4ePAgq1evxtnZ2ZL0y87OtlwNJCJFz4EDB+jVqxfDhw/nypUrfP3115w8eZKmTZvy5JNP0qFDBwICArC1teXo0aN89913SvrJXXTFn8hfNGfOHAYPHsyECRMYPXo0GRkZJCQksHv3bnx8fKhUqRIVK1a0dpgi8gD93tmTu3btomfPngQEBPDBBx/QoEED4Pa9QitXroy9vb0GYyIiIiIiIvKHtm3bRkhICEePHmX9+vV0796dadOm8eabb5KSksLOnTvp0KEDp0+fxtvbG6PRqKSfSBF36tQpPv30UwwGA2PGjAHgq6++Yvbs2Xh4eNCtWzfc3d355ptv8PDw4IUXXqB27dpWjloKIyX+RH5D3o/6R44cITExkRs3btCxY0fL8rzk38SJExk1apT1AhWRApd3fPjhhx+Ijo4mISGBV199lapVq+Lo6GhJ/jVp0oSRI0cSFBRk7ZBFRERERESkiAgPDyc1NZXAwEDGjh3Lc889x6hRoyxJP7h90uncuXMZN24ctWrVAv53KwoRKZpu3LhBmzZtiI2NpXfv3kyaNMmy7KuvvmLGjBl4eHjw/vvv07hxY+sFKkWCEn8iv5L3o/66desYPHgwHh4enD9/nqZNmzJt2jTq1auH0Whkzpw5jBgxgtGjR/PBBx9o7nSREiDv+LB27Vp69+7NI488QkxMDB4eHnTp0oXXX38dV1dXdu3aRd++falRowYTJ07UgExERERERET+UGZmJi+//DJ2dnYsWrSIZs2acfLkScusUwDp6el06tQJNzc3Vq5cqd+jRIqRgwcP8uqrr1KuXDnmz5+f71ZSGzduZMyYMdSrV48FCxbg4OCgz7/8LiX+pMT7rTOivv32Wzp37kxoaCh9+vThwIEDBAYG0rp1a0JDQ/H398dgMBAaGsrkyZOJiYmhdOnSVtoDESlIu3btonPnzowfP57evXtz4cIFvL29qVu3Ll27dmXgwIG4uLiwbds2hg8fzldffUWlSpWsHbaIiIiIiIgUYnknmh48eJDHHnuMb7/9Fjs7O5o1a8bzzz/Po48+Srly5ViwYAGXL1+23NNPV/qJFC+HDh2iZ8+eNG3alMGDB+dL/m3ZsgVfX1+qVatmxQilKFDiT0q0vMHR2bNnOXToEM899xxZWVm88847uLm5MXbsWM6cOUPbtm1p2bIlO3fupHz58syZMwd/f3+MRiPJycm6Z5dICZGbm8vChQs5duwYs2bN4vTp07Rr147HHnuM7OxsNm/ezNtvv02fPn1wd3cnPT0dBwcHa4ctIiIiIiIiRURKSgr9+vWjbNmyfPTRR2zdupWZM2fy448/UqNGDSpVqsTSpUuxs7MjJycHGxsba4csIvfZwYMH6du3LwEBAQwdOhQ/Pz9rhyRFjBJ/UuJdvHiRRo0aUa5cOd577z1ee+01IiIiqFSpEl5eXjzxxBM0atSIhQsXsm3bNtq0aUNAQACLFy+mUaNG1g5fRArYmTNnyMzMpFq1anTo0IHq1auzePFiUlNTqVmzJs7OzgQHBzN06FAATbsgIiIiIiIiv2vGjBnk5ubyyiuvULlyZQAWL17M4MGDOXjwID4+PqSlpZGZmYnJZMLJyQmA7OxsbG1trRm6iDxABw8epH///tSoUYMPP/yQOnXqWDskKUJ0HbiUeCdOnODKlSs4OzuzevVqVq1aRbt27fDz82PHjh0AvPPOOwBkZGTw7LPPkpubi4uLizXDFpECkHduzC/PkalWrRp16tQhJiaGxMRE+vXrB0BcXBxBQUG0a9eOTp06YTAYlPQTERERERGR35Wenk5CQgLjxo2jZ8+e9OnTh6tXr9KtWzdeeOEFZsyYQVZWFk5OTpQuXdqS9DObzUr6iRRz/v7+zJkzh0uXLuHm5mbtcKSIUeJPSrzWrVvz+uuvk5WVhZ2dHQsWLGD58uUAJCYmcvHiRctUfbt27aJx48ZERkZSo0YNa4YtIg9Y3v0VNm3aRK9evQgJCWHLli2WeyekpaWRkZFBTEwMN27cYPXq1Tg4ODBt2jTNtS4iIiIiIiJ/yMHBgcmTJ3PkyBFeffVVoqOjadGiBW+88QbXrl3j/PnzZGVlAflPSNVJpiIlQ1BQEJs2baJixYrWDkWKGE31KSXKr294nDdNwsaNG/nPf/5Dly5dmD9/PklJSQwdOpRWrVpRv359SpUqRYUKFThy5Ajbt2+ncePG1tsJESkwW7dupWPHjrRr146rV6+yZ88ewsLC6NOnDzdv3uSVV17hyJEj2NjYcO3aNbZs2UJAQIC1wxYRESlQe/bs4dFHH6Vdu3Zs2rTJ2uGIiIgUaQsXLuTo0aPMnj0bgHHjxjFmzBgrRyUiIkWJEn9SYuQl/c6fP09UVBQdO3a0LLt8+TItW7YkJCSEzp07079/fxISEhg1ahRNmzYlNDQUOzs7unfvTt26da23EyJSoMLDw0lJSWHw4MFcvnyZ+fPn88EHHzB37lwGDBhAamoqX331Fbdu3aJFixbUrFnT2iGLiIgUuL59++Ls7MyiRYs4duwYVatWtXZIIiIiRU7erDN5IiMjmTdvHpcvX2blypW4urpaMToRESlKNNWnlBh5ST9/f39efPFFOnTowGeffcbJkycpV64cU6dOZfXq1QCMHz8eT09Ppk+fzs6dOwkNDWXixIlK+okUc3nnwhw5coQffviBzZs3YzKZAChXrhxDhw5l3LhxBAcHExYWhrOzM126dKFHjx5K+omISImUlpbGZ599xoABA3jmmWdYunRpvuVffvkltWvXxsHBgdatW7Ns2TIMBgPXrl2zrLNnzx5atmyJg4MDVapUYfDgwaSlpRXsjoiIiFjZL5N+ZrOZoKAgBgwYwNatW4mOjrZeYCIiUuQo8SclSm5uLtWrV+fhhx8mISGBiIgInnjiCebPn096ejpubm7s37+funXrMm7cOGxtbVm2bBk3btywdugiUgAMBgPr1q0jMDCQN954gzVr1nD48GHLPRWcnJwYOnQoEydOJDg4+K4fN0VEREqa1atX4+vri6+vL926dSM8PNxyIs3Zs2d56aWX6NixI9HR0bz55pt3TVV2+PBhnnzySV588UUOHTrE6tWr2bVrFyEhIdbYHRERkULBYDBgNptp2rQp9evX5+zZs9YOSUREihBN9SklTkxMDKNGjSI3N5cePXpgNBqZOXMm7u7ufPHFFwQFBfH9999jb2/PiRMncHJyonLlytYOW0QeoLwpVS5dusRLL71E7969adSoETt27GDkyJFMnTqVYcOGWe4RmpaWxvz582nfvr2uBBYRkRKtRYsWdO7cmbfeeovs7GwqVqzIp59+Stu2bRk1ahRff/01hw8ftqz/3nvvMWHCBJKTk3F3d6dHjx44ODgwf/58yzq7du3iscceIy0tjVKlSlljt0RERAqFBQsW0L9/f2JiYjTLjIiI/GlK/EmJdOLECYYOHUpOTg4fffQRlSpV4vDhw0yYMIHOnTvTvXv3u+ZWF5HibfPmzWzevJnExETCwsJwcXEBYO7cuQwaNIgpU6YwfPhwS/JPRESkpDtx4gT169cnLi4OT09PAEJCQrh69SorV67khRdewMPDgyVLllje8+WXX/L8889bEn/16tXj559/xs7OzrKO2Wzm5s2bHDt2TCfYiIhIiXbq1CkyMzPx8/OzdigiIlKE2Fo7ABFr8PX1ZdasWYSEhDBo0CA++OADWrRowVdffWVZR0k/kZIlPj6emTNn4uHhwYULF6hTpw4AwcHBAAwfPpz09HTef/99HR9ERESAxYsXk52dTaVKlSx1ZrMZOzs7kpOTf/NEul+fd5qbm8ubb77J4MGD72q/atWqDyZwERGRIkJX+YmIyL3QZQtSYtWuXZs5c+ZgNBoZN24cu3btsnZIImJFPXv2ZPXq1SQnJ7No0SKuXLliWRYcHMyECROYNWsWycnJVoxSRESkcMjOzubjjz9m+vTpREdHW8qPP/5ItWrVWLFiBXXq1CEyMjLf+/bv35/vdUBAAEePHqVWrVp3FXt7+4LcJRERERERkWJBU31KiRcTE8OwYcNISkpixowZPPzww9YOSUQeoLz/9gwGA6dOnSIpKYmcnByaNGmCyWRiyZIl9O3bl1GjRjF8+HDKlCljeW9ycjIeHh7WCl1ERKTQWL9+Pa+88gqJiYm4ubnlWzZmzBg2btzI2rVr8fX1ZejQofTp04fo6GiGDx9OXFwc165dw83NjUOHDvHwww/z+uuv88Ybb+Dk5MTx48eJiIjgo48+stLeiYiIiIiIFF264k9KvNq1axMaGkrlypXx8vKydjgiUgAMBgNr167lmWeeoVu3bgwfPpymTZuSmJhI7969Wbp0KZMnT2bmzJkkJSVZ3qekn4iIyG2LFy+mbdu2dyX9ADp16kR0dDTJycl8/vnnrF27loYNGxIWFsaYMWMAMJlMADRs2JAdO3YQExPDo48+ir+/P++//z4VK1Ys0P0REREREREpLnTFn8gdWVlZmk5IpBjKzc3FaDRy8+ZNHB0dAdi5cycdOnRg+vTp9OrViy1btvDcc88xffp0hgwZgsFg4OOPP6ZXr1784x//YMyYMRiNOldGRETk75owYQL//ve/OX/+vLVDERERERERKZb0K6bIHUr6iRQ/eUm/qKgoGjZsyNmzZwHYt28fvXr1ol+/fiQkJBAcHMzAgQMZOnQoBoOBW7du0aNHD1asWEGnTp2U9BMREblH8+bNIzIyktOnT7N8+XJCQ0Pp2bOntcMSERERKVDe3t7MnDnT8tpgMLB+/fr7vp2xY8fSuHHj+96uiBQt+iVTRESKpbyk348//kjr1q159tln8fb2BuDYsWNkZGRw8eJFmjdvzlNPPWW5j9Dnn3/O7NmzycnJoUuXLvj5+VlxL0RERIq2mJgYnn/+efz8/Bg3bhzDhw9n7Nix1g5LRERESrhevXphMBjuKj///PMD2V5kZCT9+vW75/e3atWKIUOG3FW/dOlS3N3dLa9HjBjB1q1b/1SbShKKFF+21g5ARETkfstL+h06dIjmzZszZMgQJkyYYFneoEEDvv/+e4KCgnj66aeZP38+ZrOZW7dusW3bNuzt7cnKysLBwcGKeyEiIlL0zZgxgxkzZlg7DBEREZG7PPXUU4SHh+erK1eu3APZ1oNq99ecnZ1xdnYukG3luXXrFnZ2dgW6TRH5/+mKPxERKXaMRiPnz5+nTZs2PPPMM/mSfgsXLmTfvn0cOnSIjIwMevfuDUBaWhpjx45l3bp19O/fX0k/ERERERERkWLMZDJRoUKFfGXWrFk0aNAAJycnqlSpwsCBA0lNTbW8J+8Kuw0bNuDr64ujoyMvvfQSaWlpLFu2DG9vbzw8PBg0aBA5OTmW9/16qs9fevzxxwkJCclXd+XKFUwmE999991f2qdfX8W3fft2mjZtipOTE+7u7rRo0YJz586xdOlS/vGPf/Djjz9arnZcunQpALGxsTz//PM4Ozvj6upK586dSUhIuGsbS5YsoUaNGphMJpYtW0aZMmXIzMzMF0+nTp3o0aPHX9oHEfn7dMWfiIgUSzk5OVSvXp2MjAx2795NixYtmDRpEuPHjycyMhJXV1datmxJSEgIKSkp1KpVi+joaL7++mt8fX2tHb6IiIiIiIiIFDCj0cjs2bPx9vbmzJkzDBw4kLfffpt58+ZZ1rl58yazZ89m1apVpKSk8OKLL/Liiy/i7u7Oxo0bOX36NJ06deKRRx7hlVde+cNt9u3bl5CQEKZPn47JZAJgxYoVeHl50bp163vel+zsbDp27Mgbb7zBp59+SlZWFvv27cNgMPDKK69w5MgRNm3axLfffguAm5sbZrOZjh074uTkxI4dO8jOzmbgwIG88sorbN++3dL2zz//zGeffcaaNWuwsbGhdu3avPXWW3z55Ze8/PLLACQlJbFhwwY2bdp0z/sgIvdGiT8RESmWvL29WbFiBYMHD2bq1Kl4enryxRdfsG7dOst9+/bs2cO2bds4fPgw9evXZ+7cuVSvXt3KkYuIiIiIiIjIg7Zhw4Z802K2b9+e//znP5bX1atXZ9y4cQwYMCBf4u/WrVuEhYVRs2ZNAF566SWWL19OQkICzs7O+Pn50bp1a7Zt2/anEn+dOnVi0KBBfPHFF3Tu3BmA8PBwy30I88ybN49Fixble292djalSpX6zXZv3LjB9evXeeaZZyyx1q1b17Lc2dkZW1tbKlSoYKmLiIjg0KFDnDlzhipVqgCwfPly6tWrR2RkJEFBQQBkZWWxfPnyfFOYvvbaa4SHh1sSfytWrKBy5cq0atXqD/8GInJ/KfEnIiLFVu3atZk1axYhISF88sknjBs3jieeeAK4PTiuUKECXbp0oUuXLlaOVEREREREREQKUuvWrQkLC7O8dnJyYtu2bUycOJFjx45x48YNsrOzycjIIC0tDScnJwAcHR0tiTQAT09PvL298yURPT09SUxM/FNxmEwmunXrxpIlS+jcuTPR0dH8+OOPrF+/Pt96Xbt2ZcyYMfnq1q5dy8SJE3+z3dKlS9OrVy+efPJJ2rVrR9u2bencuTMVK1b83ViOHz9OlSpVLEk/AD8/P9zd3Tl+/Lgl8VetWrW77lv4xhtvEBQUxIULF6hUqdJvJi9FpGDoHn8iIlKs+fj4EBYWxqOPPsrWrVvZtWsXALa2tpjNZitHJyIiIiIiIiLW4OTkRK1atSwlKyuLp59+mvr167NmzRqioqKYO3cucPsqvzx2dnb52jEYDL9Zl5ub+6dj6du3LxEREcTFxbFkyRLatGlDtWrV8q3j5uaWL95atWpRvnz5/7fd8PBwfvjhB5o3b87q1avx8fFh7969v7u+2Wz+zUTdr+vzkqC/5O/vT6NGjfj44485cOAAhw8fplevXn+w5yLyICjxJyIixV7NmjWZM2cOZrOZ8ePHs3v3bgCddSYiIiIiIiIiAOzfv5/s7GymT5/Oww8/jI+PDxcvXiyQbTdo0IDAwEAWLlzIypUr6d27931r29/fn9GjR7Nnzx7q16/PypUrAbC3tycnJyffun5+fsTGxnL+/HlL3bFjx7h+/Xq+aUJ/T9++fQkPD2fJkiW0bds235WDIlJwlPgTEZESoXbt2syePRs7OztGjBjx/57hJiIiIiIiIiIlS82aNcnOzuajjz7i9OnTLF++nH//+98Ftv2+ffsyefJkcnJyeOGFF/52e2fOnGH06NH88MMPnDt3ji1btnDy5ElLAs/b25szZ84QHR1NUlISmZmZtG3bloYNG9K1a1cOHDjAvn376NGjB4899hiBgYF/uM2uXbty4cIFFi5ceF+TlyLy1yjxJyIiJUbt2rUJDQ2lcuXKeHl5WTscERERERERESkkGjduzL/+9S+mTJlC/fr1WbFiBZMmTSqw7Xfp0gVbW1tee+01SpUq9bfbc3R05KeffqJTp074+PjQr18/QkJCePPNNwHo1KkTTz31FK1bt6ZcuXJ8+umnGAwG1q9fj4eHBy1btqRt27bUqFGD1atX/6lturq60qlTJ5ydnenYsePf3gcRuTcGs25wJCIiJUxWVhb29vbWDkNEREREREREBIDz58/j7e1NZGQkAQEB1g7nnrVr1466desye/Zsa4ciUmIp8SciIiIiIiIiIiIiYgW3bt3i0qVLjBo1inPnzrF7925rh3RPrl69ypYtW+jatSvHjh3D19fX2iGJlFi21g5ARERERERERERERKQk2r17N61bt8bHx4fPP//c2uHcs4CAAJKTk5kyZYqSfiJWpiv+RERERERERERERERERIoBo7UDEBEREREREREREREREZG/T4k/ERERERERERERERERkWJAiT8RERERERERERERERGRYkCJPxEREREREREREREREZFiQIk/EZH7qFWrVgwZMsTaYYiIiIiIiIiIiIhICaTEn4gUO/Hx8bz11lvUqlWLUqVK4enpySOPPMK///1vbt68ae3wREREREREREREREQeCFtrByAicj+dPn2aFi1a4O7uzsSJE2nQoAHZ2dmcPHmSJUuW4OXlxXPPPWftMH9XTk4OBoMBo1HnZYiIiIiIiIiIiIjIX6NflkWkWBk4cCC2trbs37+fzp07U7duXRo0aECnTp34+uuvefbZZwG4fv06/fr1o3z58ri6uvL444/z448/WtoZO3YsjRs3Zvny5Xh7e+Pm5sarr75KSkqKZZ20tDR69OiBs7MzFStWZPr06XfFk5WVxdtvv02lSpVwcnLioYceYvv27ZblS5cuxd3dnQ0bNuDn54fJZOLcuXMP7g8kIiIiIiIiIiIiIsWWEn8iUmxcuXKFLVu2EBwcjJOT02+uYzAYMJvNdOjQgfj4eDZu3EhUVBQBAQG0adOGq1evWtY9deoU69evZ8OGDWzYsIEdO3YwefJky/KRI0eybds21q1bx5YtW9i+fTtRUVH5tvf666+ze/duVq1axaFDh3j55Zd56qmniImJsaxz8+ZNJk2axKJFizh69Cjly5e/z38ZERERERERERERESkJNNWniBQbP//8M2azGV9f33z1ZcuWJSMjA4Dg4GCefPJJDh8+TGJiIiaTCYBp06axfv16Pv/8c/r16wdAbm4uS5cuxcXFBYDu3buzdetWJkyYQGpqKosXL+bjjz+mXbt2ACxbtozKlStbtnvq1Ck+/fRT4uLi8PLyAmDEiBFs2rSJ8PBwJk6cCMCtW7eYN28ejRo1eoB/HREREREREREREREp7pT4E5Fix2Aw5Hu9b98+cnNz6dq1K5mZmURFRZGamkqZMmXyrZeens6pU6csr729vS1JP4CKFSuSmJgI3E7qZWVl0axZM8vy0qVL50s6HjhwALPZjI+PT77tZGZm5tu2vb09DRs2/Bt7LCIiIiIiIiIiIiKixJ+IFCO1atXCYDDw008/5auvUaMGAA4ODsDtK/kqVqyY7157edzd3S3P7ezs8i0zGAzk5uYCYDab/zCe3NxcbGxsiIqKwsbGJt8yZ2dny3MHB4e7kpUiIiIiIiIiIiIiIn+VEn8iUmyUKVOGdu3aMWfOHAYNGvS79/kLCAggPj4eW1tbvL2972lbtWrVws7Ojr1791K1alUAkpOTOXnyJI899hgA/v7+5OTkkJiYyKOPPnpP2xERERERERERERER+bOM1g5AROR+mjdvHtnZ2QQGBrJ69WqOHz/OiRMn+OSTT/jpp5+wsbGhbdu2NGvWjI4dO7J582bOnj3Lnj17eO+999i/f/+f2o6zszN9+vRh5MiRbN26lSNHjtCrVy+Mxv8dVn18fOjatSs9evRg7dq1nDlzhsjISKZMmcLGjRsf1J9AREREREREREREREooXfEnIsVKzZo1OXjwIBMnTmT06NHExcVhMpnw8/NjxIgRDBw4EIPBwMaNGxkzZgy9e/fm8uXLVKhQgZYtW+Lp6fmntxUaGkpqairPPfccLi4uDB8+nOvXr+dbJzw8nPHjxzN8+HAuXLhAmTJlaNasGU8//fT93nURERERERERERERKeEM5j9zoyoRERERERERERERERERKdQ01aeIiIiIiIiIiIiIiIhIMaDEn4iIiIiIiIiIiIiIiEgxoMSfiIiIiIiIiIiIiIiISDGgxJ+IiIiIiIiIiIiIiIhIMaDEn4iIiIiIiIiIiIiIiEgxoMSfiIiIiIiIiIiIiIiISDGgxJ+IiIiIiIiIiIiIiIhIMaDEn4iIiIiIiIiIiIiIiEgxoMSfiIiIiIiIiIiIiIiISDGgxJ+IiIiIiIiIiIiIiIhIMaDEn4iIiIiIiIiIiIiIiEgx8H+Ky/hbspZQIwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1800x400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x1200 with 7 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x800 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Age'] = df['Age'].round(0).astype(int)\n",
    "\n",
    "custom_orders = {\n",
    "    \"Gender\": [\"Male\", \"Female\"],\n",
    "    \"FamilyHistory\": [\"yes\", \"no\"],\n",
    "    \"HighCalorieFood\": [\"yes\", \"no\"],\n",
    "    \"VegetablesPerDay\": [\"Never\", \"Sometimes\", \"Always\"],\n",
    "    \"MainMealsPerDay\": [\"One\", \"Two\", \"Three\", \"More than three\"],\n",
    "    \"Snack\": [\"no\", \"Sometimes\", \"Frequently\", \"Always\"],\n",
    "    \"Smoke\": [\"yes\", \"no\"],\n",
    "    \"WaterIntake\": [\"Less than a liter\", \"Between 1 and 2 L\", \"More than 2 L\"],\n",
    "    \"Alcohol\": [\"no\", \"Sometimes\", \"Frequently\", \"Always\"],\n",
    "    \"CaloriesMonitor\": [\"yes\", \"no\"],\n",
    "    \"PhysicalActivity\": [\n",
    "        \"I do not have\", \n",
    "        \"1 or 2 days\", \n",
    "        \"2 or 4 days\", \n",
    "        \"4 or 5 days\"\n",
    "    ],\n",
    "    \"TechUsageTime\": [\"0–2 hours\", \"3–5 hours\", \"More than 5 hours\"],\n",
    "    \"TransportMode\":  [\n",
    "        \"Automobile\", \"Motorbike\", \"Bike\", \n",
    "        \"Public_Transportation\", \"Walking\"\n",
    "    ],\n",
    "    \"ObesityLevel\": [\n",
    "        \"Insufficient_Weight\", \"Normal_Weight\", \n",
    "        \"Overweight_Level_I\", \"Overweight_Level_II\", \n",
    "        \"Obesity_Type_I\", \"Obesity_Type_II\", \"Obesity_Type_III\"\n",
    "    ]\n",
    "}\n",
    "grouped_vars = {\n",
    "    \"Responder Characteristics\": [\n",
    "        \"Gender\", \"Age\", \"FamilyHistory\"\n",
    "    ],\n",
    "    \"Eating Habits\": [\n",
    "        \"HighCalorieFood\", \"VegetablesPerDay\", \"MainMealsPerDay\", \"Snack\", \"Smoke\", \"WaterIntake\", \"Alcohol\"\n",
    "    ],\n",
    "    \"Physical Conditioning\": [\n",
    "        \"CaloriesMonitor\", \"PhysicalActivity\", \"TechUsageTime\", \"TransportMode\"\n",
    "    ],\n",
    "    \"Target Variable\": [\n",
    "        \"ObesityLevel\"\n",
    "    ]\n",
    "}\n",
    "for group, cols in grouped_vars.items():\n",
    "    n = len(cols)\n",
    "    rows = (n + 2) // 3\n",
    "    fig, axes = plt.subplots(rows, 3, figsize=(18, 4 * rows))\n",
    "    axes = axes.flatten()\n",
    "    fig.suptitle(group, fontsize=20)\n",
    "\n",
    "    for i, col in enumerate(cols):\n",
    "        if df[col].dtype == 'object' or str(df[col].dtype) == 'category':\n",
    "            order = custom_orders.get(col)  # ← 关键修改点\n",
    "            sns.countplot(data=df, x=col, ax=axes[i], order=order)\n",
    "            axes[i].tick_params(axis='x', rotation=45)\n",
    "        else:\n",
    "            sns.histplot(data=df, x=col, ax=axes[i], bins=20, kde=True)\n",
    "        axes[i].set_title(col)\n",
    "        axes[i].set_ylabel(\"Count\")\n",
    "\n",
    "    for j in range(i + 1, len(axes)):\n",
    "        fig.delaxes(axes[j])\n",
    "\n",
    "    plt.tight_layout(rect=[0, 0, 1, 0.95])\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f74f9ad6-b0de-409f-ad11-2b702172a72e",
   "metadata": {},
   "source": [
    "#### 2.2.2.2 Visualization of Correlation Matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3db4e727-dd63-4cb0-be07-c2a7426d133f",
   "metadata": {},
   "source": [
    "Pearson correlation coefficient is only applicable to continuous numerical variables and cannot be directly used for categorical variables (especially multi-class ones). Therefore, we employed a combined correlation matrix using Cramér's V and η² to evaluate the relationship strength between categorical features and the target variable (ObesityLevel).\n",
    "\n",
    "The specific analysis methods are as follows:\n",
    "- **Cramér's V**: Measures the association strength between two categorical variables, with a value range of [0, 1]. Higher values indicate stronger relationships;\n",
    "- **η² (Eta-squared)**: A statistical measure for assessing the strength of relationship between continuous and categorical variables, commonly used in ANOVA. Here we use it to evaluate the association strength between Age and other categorical variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "7edcac3a-53d9-4f52-a8ce-2f261f1f0f46",
   "metadata": {},
   "outputs": [],
   "source": [
    "def cramers_v(x, y):\n",
    "    confusion_matrix = pd.crosstab(x, y)\n",
    "    chi2 = chi2_contingency(confusion_matrix)[0]\n",
    "    n = confusion_matrix.sum().sum()\n",
    "    phi2 = chi2 / n\n",
    "    r, k = confusion_matrix.shape\n",
    "    phi2corr = max(0, phi2 - ((k - 1)*(r - 1)) / (n - 1))\n",
    "    rcorr = r - ((r - 1)**2) / (n - 1)\n",
    "    kcorr = k - ((k - 1)**2) / (n - 1)\n",
    "    return np.sqrt(phi2corr / min((kcorr - 1), (rcorr - 1)))\n",
    "    \n",
    "# Numerical variable vs categorical variable\n",
    "def eta_squared(y, x):\n",
    "    groups = [y[x == level] for level in np.unique(x)]\n",
    "    grand_mean = y.mean()\n",
    "    ss_between = sum([len(g) * (g.mean() - grand_mean)**2 for g in groups])\n",
    "    ss_total = sum((y - grand_mean)**2)\n",
    "    return ss_between / ss_total if ss_total != 0 else 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "b003e0f7-8f78-40a0-9078-49569bbb489a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# All categorical variable column names\n",
    "cat_cols = df.select_dtypes(include=['object', 'category']).columns.tolist()\n",
    "\n",
    "# Initialize Cramér's V matrix\n",
    "cramer_matrix = pd.DataFrame(index=cat_cols, columns=cat_cols)\n",
    "\n",
    "# categorical variable vs categorical variable\n",
    "for col1 in cat_cols:\n",
    "    for col2 in cat_cols:\n",
    "        cramer_matrix.loc[col1, col2] = cramers_v(df[col1], df[col2])\n",
    "\n",
    "# Calculate η ² for all categorical variables using Age and add it as a new line\n",
    "age_eta_row = {}\n",
    "for col in cat_cols:\n",
    "    age_eta_row[col] = eta_squared(df['Age'], df[col])\n",
    "cramer_matrix.loc['Age'] = age_eta_row\n",
    "\n",
    "# Add Age column\n",
    "age_eta_col = [eta_squared(df['Age'], df[col]) for col in cat_cols] + [1.0]\n",
    "cramer_matrix['Age'] = age_eta_col\n",
    "new_order = ['Age'] + [col for col in cramer_matrix.columns if col != 'Age']\n",
    "cramer_matrix = cramer_matrix.reindex(index=new_order, columns=new_order)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "0b6e2f92-2a7d-4b08-b3ad-de660f41f45e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Convert to floating point number\n",
    "cramer_matrix = cramer_matrix.astype(float)\n",
    "\n",
    "# Visualization\n",
    "plt.figure(figsize=(12, 10))\n",
    "sns.heatmap(cramer_matrix, annot=True, cmap='coolwarm', center=0.5, square=True)\n",
    "plt.title(\"Combined Cramér's V + Eta² Correlation Matrix\", fontsize=16)\n",
    "plt.xticks(rotation=45, ha='right')\n",
    "plt.yticks(rotation=0)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "153410de-1a7a-4d0c-9401-d8c3d15c0bbc",
   "metadata": {},
   "source": [
    "The correlation heatmap based on Cramér's V and η² reveals that Gender, FamilyHistory, VegetablesPerDay, Snack, and HighCalorieFood exhibit strong correlations with the target variable ObesityLevel, identifying them as core features. \n",
    "\n",
    "In contrast, Smoke demonstrates negligible correlation (0.11) and is recommended for removal during model training. Other weakly correlated features such as TransportMode and TechUsageTime may be further evaluated during the modeling process to determine their retention value."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a229bbc-0185-4c31-831f-ef138fc73a80",
   "metadata": {},
   "source": [
    "### 2.2.3 Feature Engineering"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aac60f78-f786-43f6-97f9-6e5f1bc6db06",
   "metadata": {},
   "source": [
    "The ObesityLevel target label is derived from BMI values calculated using height and weight measurements. Since these two variables have already been fully utilized in label generation, we have decided to exclude them from the modeling features to prevent potential overfitting caused by the model repeatedly calculating labels during training.\n",
    "\n",
    "The correlation of smoke can be ignored, so we delete this column.\n",
    "\n",
    "Other remaining features will be used to investigate their relationships with obesity levels, while avoiding data leakage from the height/weight-based label calculation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "5342f265-99c9-4571-88e2-2b101111fb10",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>FamilyHistory</th>\n",
       "      <th>HighCalorieFood</th>\n",
       "      <th>VegetablesPerDay</th>\n",
       "      <th>MainMealsPerDay</th>\n",
       "      <th>Snack</th>\n",
       "      <th>WaterIntake</th>\n",
       "      <th>CaloriesMonitor</th>\n",
       "      <th>PhysicalActivity</th>\n",
       "      <th>TechUsageTime</th>\n",
       "      <th>Alcohol</th>\n",
       "      <th>TransportMode</th>\n",
       "      <th>ObesityLevel</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Female</td>\n",
       "      <td>21</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>Sometimes</td>\n",
       "      <td>Three</td>\n",
       "      <td>Sometimes</td>\n",
       "      <td>Between 1 and 2 L</td>\n",
       "      <td>no</td>\n",
       "      <td>I do not have</td>\n",
       "      <td>3–5 hours</td>\n",
       "      <td>no</td>\n",
       "      <td>Public_Transportation</td>\n",
       "      <td>Normal_Weight</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Female</td>\n",
       "      <td>21</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>Always</td>\n",
       "      <td>Three</td>\n",
       "      <td>Sometimes</td>\n",
       "      <td>More than 2 L</td>\n",
       "      <td>yes</td>\n",
       "      <td>4 or 5 days</td>\n",
       "      <td>0–2 hours</td>\n",
       "      <td>Sometimes</td>\n",
       "      <td>Public_Transportation</td>\n",
       "      <td>Normal_Weight</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Male</td>\n",
       "      <td>23</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>Sometimes</td>\n",
       "      <td>Three</td>\n",
       "      <td>Sometimes</td>\n",
       "      <td>Between 1 and 2 L</td>\n",
       "      <td>no</td>\n",
       "      <td>2 or 4 days</td>\n",
       "      <td>3–5 hours</td>\n",
       "      <td>Frequently</td>\n",
       "      <td>Public_Transportation</td>\n",
       "      <td>Normal_Weight</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Male</td>\n",
       "      <td>27</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>Always</td>\n",
       "      <td>Three</td>\n",
       "      <td>Sometimes</td>\n",
       "      <td>Between 1 and 2 L</td>\n",
       "      <td>no</td>\n",
       "      <td>2 or 4 days</td>\n",
       "      <td>0–2 hours</td>\n",
       "      <td>Frequently</td>\n",
       "      <td>Walking</td>\n",
       "      <td>Overweight_Level_I</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Male</td>\n",
       "      <td>22</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>Sometimes</td>\n",
       "      <td>One</td>\n",
       "      <td>Sometimes</td>\n",
       "      <td>Between 1 and 2 L</td>\n",
       "      <td>no</td>\n",
       "      <td>I do not have</td>\n",
       "      <td>0–2 hours</td>\n",
       "      <td>Sometimes</td>\n",
       "      <td>Public_Transportation</td>\n",
       "      <td>Overweight_Level_II</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Gender  Age FamilyHistory HighCalorieFood VegetablesPerDay MainMealsPerDay  \\\n",
       "0  Female   21           yes              no        Sometimes           Three   \n",
       "1  Female   21           yes              no           Always           Three   \n",
       "2    Male   23           yes              no        Sometimes           Three   \n",
       "3    Male   27            no              no           Always           Three   \n",
       "4    Male   22            no              no        Sometimes             One   \n",
       "\n",
       "       Snack        WaterIntake CaloriesMonitor PhysicalActivity  \\\n",
       "0  Sometimes  Between 1 and 2 L              no    I do not have   \n",
       "1  Sometimes      More than 2 L             yes      4 or 5 days   \n",
       "2  Sometimes  Between 1 and 2 L              no      2 or 4 days   \n",
       "3  Sometimes  Between 1 and 2 L              no      2 or 4 days   \n",
       "4  Sometimes  Between 1 and 2 L              no    I do not have   \n",
       "\n",
       "  TechUsageTime     Alcohol          TransportMode         ObesityLevel  \n",
       "0     3–5 hours          no  Public_Transportation        Normal_Weight  \n",
       "1     0–2 hours   Sometimes  Public_Transportation        Normal_Weight  \n",
       "2     3–5 hours  Frequently  Public_Transportation        Normal_Weight  \n",
       "3     0–2 hours  Frequently                Walking   Overweight_Level_I  \n",
       "4     0–2 hours   Sometimes  Public_Transportation  Overweight_Level_II  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.drop(['Height', 'Weight', 'Smoke'], axis=1)\n",
    "df.to_csv(\"obesity_data_cleaned.csv\", index=False)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3eabc62a-9515-4201-9216-1e7df3d88538",
   "metadata": {},
   "source": [
    "### 2.2.4 Encoding categorical data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "54d44c3d-4b33-41d8-8028-200ffa785c92",
   "metadata": {},
   "source": [
    "This study adopted multiple encoding strategies for categorical variables.   \n",
    "\n",
    "For variables with Boolean semantics (such as FamilyHistory, HighCalorieFood, etc.), 0/1 mapping encoding was used for simplicity and clarity. For unordered multi-category variables (such as Gender, TransportMode) and variables with ambiguous ordinal relationships (such as Snack, Alcohol), one-hot encoding was applied to avoid false ordinal assumptions.  \n",
    "\n",
    "Additionally, LabelEncoder was used to convert the target variable ObesityLevel into integer labels to facilitate classification model training.\n",
    "\n",
    "Below are two examples of one-hot encoding:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4b1529c-f20c-45cc-a2da-c91e516a4c68",
   "metadata": {},
   "source": [
    "| Variable       | Category              | Encoding                         |\n",
    "|----------------|-----------------------|----------------------------------|\n",
    "| Gender         | Female                | 0 (Gender_Male = 0)              |\n",
    "|                | Male                  | 1 (Gender_Male = 1)              |\n",
    "| TransportMode  | Automobile            | 0000 (all four dummy columns = 0)|\n",
    "|                | Bike                  | 1000 (only TransportMode_Bike = 1)      |\n",
    "|                | Motorbike             | 0100 (only TransportMode_Motorbike = 1) |\n",
    "|                | Public Transportation | 0010 (only TransportMode_Public_Transportation = 1) |\n",
    "|                | Walking               | 0001 (only TransportMode_Walking = 1) |\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "14fbb81c-077f-405e-85b3-c3a2ac5ebc6c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>FamilyHistory</th>\n",
       "      <th>HighCalorieFood</th>\n",
       "      <th>CaloriesMonitor</th>\n",
       "      <th>ObesityLevel</th>\n",
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       "      <th>TransportMode_Public_Transportation</th>\n",
       "      <th>TransportMode_Walking</th>\n",
       "      <th>...</th>\n",
       "      <th>MainMealsPerDay_Two</th>\n",
       "      <th>MainMealsPerDay_Three</th>\n",
       "      <th>MainMealsPerDay_More than three</th>\n",
       "      <th>WaterIntake_Between 1 and 2 L</th>\n",
       "      <th>WaterIntake_More than 2 L</th>\n",
       "      <th>PhysicalActivity_1 or 2 days</th>\n",
       "      <th>PhysicalActivity_2 or 4 days</th>\n",
       "      <th>PhysicalActivity_4 or 5 days</th>\n",
       "      <th>TechUsageTime_3–5 hours</th>\n",
       "      <th>TechUsageTime_More than 5 hours</th>\n",
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       "      <th>2</th>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>2082</th>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2083</th>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2084</th>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2085</th>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2086</th>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2087 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Age FamilyHistory HighCalorieFood CaloriesMonitor  ObesityLevel  \\\n",
       "0      21             1               0               0             1   \n",
       "1      21             1               0               1             1   \n",
       "2      23             1               0               0             1   \n",
       "3      27             0               0               0             5   \n",
       "4      22             0               0               0             6   \n",
       "...   ...           ...             ...             ...           ...   \n",
       "2082   21             1               1               0             4   \n",
       "2083   22             1               1               0             4   \n",
       "2084   23             1               1               0             4   \n",
       "2085   24             1               1               0             4   \n",
       "2086   24             1               1               0             4   \n",
       "\n",
       "      Gender_Male  TransportMode_Bike  TransportMode_Motorbike  \\\n",
       "0               0                   0                        0   \n",
       "1               0                   0                        0   \n",
       "2               1                   0                        0   \n",
       "3               1                   0                        0   \n",
       "4               1                   0                        0   \n",
       "...           ...                 ...                      ...   \n",
       "2082            0                   0                        0   \n",
       "2083            0                   0                        0   \n",
       "2084            0                   0                        0   \n",
       "2085            0                   0                        0   \n",
       "2086            0                   0                        0   \n",
       "\n",
       "      TransportMode_Public_Transportation  TransportMode_Walking  ...  \\\n",
       "0                                       1                      0  ...   \n",
       "1                                       1                      0  ...   \n",
       "2                                       1                      0  ...   \n",
       "3                                       0                      1  ...   \n",
       "4                                       1                      0  ...   \n",
       "...                                   ...                    ...  ...   \n",
       "2082                                    1                      0  ...   \n",
       "2083                                    1                      0  ...   \n",
       "2084                                    1                      0  ...   \n",
       "2085                                    1                      0  ...   \n",
       "2086                                    1                      0  ...   \n",
       "\n",
       "      MainMealsPerDay_Two  MainMealsPerDay_Three  \\\n",
       "0                       0                      1   \n",
       "1                       0                      1   \n",
       "2                       0                      1   \n",
       "3                       0                      1   \n",
       "4                       0                      0   \n",
       "...                   ...                    ...   \n",
       "2082                    0                      1   \n",
       "2083                    0                      1   \n",
       "2084                    0                      1   \n",
       "2085                    0                      1   \n",
       "2086                    0                      1   \n",
       "\n",
       "      MainMealsPerDay_More than three  WaterIntake_Between 1 and 2 L  \\\n",
       "0                                   0                              1   \n",
       "1                                   0                              0   \n",
       "2                                   0                              1   \n",
       "3                                   0                              1   \n",
       "4                                   0                              1   \n",
       "...                               ...                            ...   \n",
       "2082                                0                              1   \n",
       "2083                                0                              1   \n",
       "2084                                0                              1   \n",
       "2085                                0                              0   \n",
       "2086                                0                              0   \n",
       "\n",
       "      WaterIntake_More than 2 L  PhysicalActivity_1 or 2 days  \\\n",
       "0                             0                             0   \n",
       "1                             1                             0   \n",
       "2                             0                             0   \n",
       "3                             0                             0   \n",
       "4                             0                             0   \n",
       "...                         ...                           ...   \n",
       "2082                          0                             0   \n",
       "2083                          0                             1   \n",
       "2084                          0                             1   \n",
       "2085                          1                             1   \n",
       "2086                          1                             1   \n",
       "\n",
       "      PhysicalActivity_2 or 4 days  PhysicalActivity_4 or 5 days  \\\n",
       "0                                0                             0   \n",
       "1                                0                             1   \n",
       "2                                1                             0   \n",
       "3                                1                             0   \n",
       "4                                0                             0   \n",
       "...                            ...                           ...   \n",
       "2082                             1                             0   \n",
       "2083                             0                             0   \n",
       "2084                             0                             0   \n",
       "2085                             0                             0   \n",
       "2086                             0                             0   \n",
       "\n",
       "      TechUsageTime_3–5 hours  TechUsageTime_More than 5 hours  \n",
       "0                           1                                0  \n",
       "1                           0                                0  \n",
       "2                           1                                0  \n",
       "3                           0                                0  \n",
       "4                           0                                0  \n",
       "...                       ...                              ...  \n",
       "2082                        1                                0  \n",
       "2083                        1                                0  \n",
       "2084                        1                                0  \n",
       "2085                        1                                0  \n",
       "2086                        1                                0  \n",
       "\n",
       "[2087 rows x 28 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Boolean mapping\n",
    "df['FamilyHistory'] = df['FamilyHistory'].map({'yes': 1, 'no': 0})\n",
    "df['HighCalorieFood'] = df['HighCalorieFood'].map({'yes': 1, 'no': 0})\n",
    "df['CaloriesMonitor'] = df['CaloriesMonitor'].map({'yes': 1, 'no': 0})\n",
    "\n",
    "# One-hot encoding for Gender and TransportMode\n",
    "df = pd.get_dummies(df, columns=['Gender', 'TransportMode','Snack','Alcohol','VegetablesPerDay','MainMealsPerDay','WaterIntake','PhysicalActivity','TechUsageTime'], drop_first=True)\n",
    "bool_cols = df.select_dtypes(include=['bool', 'uint8']).columns\n",
    "df[bool_cols] = df[bool_cols].astype(int)\n",
    "\n",
    "# Label encode the target\n",
    "# ordered_labels = [\n",
    "#     'Insufficient_Weight', 'Normal_Weight', 'Overweight_Level_I',\n",
    "#     'Overweight_Level_II', 'Obesity_Type_I', 'Obesity_Type_II', 'Obesity_Type_III'\n",
    "# ]\n",
    "# label_map = {label: idx for idx, label in enumerate(ordered_labels)}\n",
    "# df['ObesityLevel'] = df['ObesityLevel'].map(label_map)\n",
    "le = LabelEncoder()\n",
    "df['ObesityLevel'] = le.fit_transform(df['ObesityLevel'])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "1aa1a1e9-e57d-4b7b-90a7-53d79a13959a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2087 entries, 0 to 2086\n",
      "Data columns (total 28 columns):\n",
      " #   Column                               Non-Null Count  Dtype   \n",
      "---  ------                               --------------  -----   \n",
      " 0   Age                                  2087 non-null   int32   \n",
      " 1   FamilyHistory                        2087 non-null   category\n",
      " 2   HighCalorieFood                      2087 non-null   category\n",
      " 3   CaloriesMonitor                      2087 non-null   category\n",
      " 4   ObesityLevel                         2087 non-null   int32   \n",
      " 5   Gender_Male                          2087 non-null   int32   \n",
      " 6   TransportMode_Bike                   2087 non-null   int32   \n",
      " 7   TransportMode_Motorbike              2087 non-null   int32   \n",
      " 8   TransportMode_Public_Transportation  2087 non-null   int32   \n",
      " 9   TransportMode_Walking                2087 non-null   int32   \n",
      " 10  Snack_Frequently                     2087 non-null   int32   \n",
      " 11  Snack_Sometimes                      2087 non-null   int32   \n",
      " 12  Snack_no                             2087 non-null   int32   \n",
      " 13  Alcohol_Frequently                   2087 non-null   int32   \n",
      " 14  Alcohol_Sometimes                    2087 non-null   int32   \n",
      " 15  Alcohol_no                           2087 non-null   int32   \n",
      " 16  VegetablesPerDay_Sometimes           2087 non-null   int32   \n",
      " 17  VegetablesPerDay_Always              2087 non-null   int32   \n",
      " 18  MainMealsPerDay_Two                  2087 non-null   int32   \n",
      " 19  MainMealsPerDay_Three                2087 non-null   int32   \n",
      " 20  MainMealsPerDay_More than three      2087 non-null   int32   \n",
      " 21  WaterIntake_Between 1 and 2 L        2087 non-null   int32   \n",
      " 22  WaterIntake_More than 2 L            2087 non-null   int32   \n",
      " 23  PhysicalActivity_1 or 2 days         2087 non-null   int32   \n",
      " 24  PhysicalActivity_2 or 4 days         2087 non-null   int32   \n",
      " 25  PhysicalActivity_4 or 5 days         2087 non-null   int32   \n",
      " 26  TechUsageTime_3–5 hours              2087 non-null   int32   \n",
      " 27  TechUsageTime_More than 5 hours      2087 non-null   int32   \n",
      "dtypes: category(3), int32(25)\n",
      "memory usage: 210.4 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dcfd7aab-aae8-43c4-9dba-eb13c840d714",
   "metadata": {},
   "source": [
    "### 2.2.5 Feature Scaling"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "993b1239-2943-4bfb-b0b5-48cf6e6f226d",
   "metadata": {},
   "source": [
    "We use StandardScaler to scale Age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "d67e3832-aaa2-4797-bdb6-7eb28f33bdc9",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>FamilyHistory</th>\n",
       "      <th>HighCalorieFood</th>\n",
       "      <th>CaloriesMonitor</th>\n",
       "      <th>ObesityLevel</th>\n",
       "      <th>Gender_Male</th>\n",
       "      <th>TransportMode_Bike</th>\n",
       "      <th>TransportMode_Motorbike</th>\n",
       "      <th>TransportMode_Public_Transportation</th>\n",
       "      <th>TransportMode_Walking</th>\n",
       "      <th>...</th>\n",
       "      <th>MainMealsPerDay_Two</th>\n",
       "      <th>MainMealsPerDay_Three</th>\n",
       "      <th>MainMealsPerDay_More than three</th>\n",
       "      <th>WaterIntake_Between 1 and 2 L</th>\n",
       "      <th>WaterIntake_More than 2 L</th>\n",
       "      <th>PhysicalActivity_1 or 2 days</th>\n",
       "      <th>PhysicalActivity_2 or 4 days</th>\n",
       "      <th>PhysicalActivity_4 or 5 days</th>\n",
       "      <th>TechUsageTime_3–5 hours</th>\n",
       "      <th>TechUsageTime_More than 5 hours</th>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.414444</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <td>0</td>\n",
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       "</table>\n",
       "<p>5 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Age FamilyHistory HighCalorieFood CaloriesMonitor  ObesityLevel  \\\n",
       "0 -0.526224             1               0               0             1   \n",
       "1 -0.526224             1               0               1             1   \n",
       "2 -0.212668             1               0               0             1   \n",
       "3  0.414444             0               0               0             5   \n",
       "4 -0.369446             0               0               0             6   \n",
       "\n",
       "   Gender_Male  TransportMode_Bike  TransportMode_Motorbike  \\\n",
       "0            0                   0                        0   \n",
       "1            0                   0                        0   \n",
       "2            1                   0                        0   \n",
       "3            1                   0                        0   \n",
       "4            1                   0                        0   \n",
       "\n",
       "   TransportMode_Public_Transportation  TransportMode_Walking  ...  \\\n",
       "0                                    1                      0  ...   \n",
       "1                                    1                      0  ...   \n",
       "2                                    1                      0  ...   \n",
       "3                                    0                      1  ...   \n",
       "4                                    1                      0  ...   \n",
       "\n",
       "   MainMealsPerDay_Two  MainMealsPerDay_Three  \\\n",
       "0                    0                      1   \n",
       "1                    0                      1   \n",
       "2                    0                      1   \n",
       "3                    0                      1   \n",
       "4                    0                      0   \n",
       "\n",
       "   MainMealsPerDay_More than three  WaterIntake_Between 1 and 2 L  \\\n",
       "0                                0                              1   \n",
       "1                                0                              0   \n",
       "2                                0                              1   \n",
       "3                                0                              1   \n",
       "4                                0                              1   \n",
       "\n",
       "   WaterIntake_More than 2 L  PhysicalActivity_1 or 2 days  \\\n",
       "0                          0                             0   \n",
       "1                          1                             0   \n",
       "2                          0                             0   \n",
       "3                          0                             0   \n",
       "4                          0                             0   \n",
       "\n",
       "   PhysicalActivity_2 or 4 days  PhysicalActivity_4 or 5 days  \\\n",
       "0                             0                             0   \n",
       "1                             0                             1   \n",
       "2                             1                             0   \n",
       "3                             1                             0   \n",
       "4                             0                             0   \n",
       "\n",
       "   TechUsageTime_3–5 hours  TechUsageTime_More than 5 hours  \n",
       "0                        1                                0  \n",
       "1                        0                                0  \n",
       "2                        1                                0  \n",
       "3                        0                                0  \n",
       "4                        0                                0  \n",
       "\n",
       "[5 rows x 28 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler = StandardScaler()\n",
    "df['Age'] = scaler.fit_transform(df['Age'].values.reshape(-1, 1))\n",
    "df.head()\n",
    "# scaler = MinMaxScaler()\n",
    "# df[['Age', 'Height', 'Weight']] = scaler.fit_transform(df[['Age', 'Height', 'Weight']])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c145f83d-705b-4032-ab18-c9e7486d636d",
   "metadata": {},
   "source": [
    "### 2.2.6 Handling Imbalanced Data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3651f145-89b6-4095-84ed-e0aa80e9840f",
   "metadata": {},
   "source": [
    "The data itself was processed using SMOTE upsampling technique, so the label data is very balanced."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "43f3b530-0006-4728-8fc4-9872b7c398db",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ObesityLevel\n",
       "2    351\n",
       "4    324\n",
       "3    297\n",
       "6    290\n",
       "1    282\n",
       "5    276\n",
       "0    267\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['ObesityLevel'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b4f7ba7-ecc7-4f76-abdd-6235eded1c45",
   "metadata": {},
   "source": [
    "# 3 Data Modeling"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa107555-ada4-45d4-b8a7-feb0ba9ce546",
   "metadata": {},
   "source": [
    "## 3.1 Divide the training set and test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "e2e2c7eb-e656-45ee-925f-3b3db9b14a33",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop('ObesityLevel', axis=1)\n",
    "y = df['ObesityLevel']\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d95f48a-5829-4f47-bc2c-135001a9eaa8",
   "metadata": {},
   "source": [
    "## 3.2 Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "ed9d802b-bb68-4630-8139-be7491ce07e4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.63      0.68      0.65        53\n",
      "           1       0.59      0.46      0.51        57\n",
      "           2       0.58      0.74      0.65        70\n",
      "           3       0.61      0.90      0.72        60\n",
      "           4       0.89      0.98      0.93        65\n",
      "           5       0.56      0.44      0.49        55\n",
      "           6       0.38      0.16      0.22        58\n",
      "\n",
      "    accuracy                           0.63       418\n",
      "   macro avg       0.61      0.62      0.60       418\n",
      "weighted avg       0.61      0.63      0.61       418\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# initial model\n",
    "log_clf = LogisticRegression(max_iter=1000)\n",
    "log_clf.fit(X_train, y_train)\n",
    "\n",
    "# prediction\n",
    "y_pred_log = log_clf.predict(X_test)\n",
    "\n",
    "# report\n",
    "print(\"Logistic Regression:\")\n",
    "print(classification_report(y_test, y_pred_log))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "04a2ae6f-8dd6-4502-b30d-642ef0803431",
   "metadata": {},
   "source": [
    "## 3.3 Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "1783827b-1bf2-4524-a5eb-1b34ed183403",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.80      0.91      0.85        53\n",
      "           1       0.60      0.61      0.61        57\n",
      "           2       0.71      0.86      0.77        70\n",
      "           3       0.87      0.90      0.89        60\n",
      "           4       0.98      0.98      0.98        65\n",
      "           5       0.72      0.56      0.63        55\n",
      "           6       0.73      0.57      0.64        58\n",
      "\n",
      "    accuracy                           0.78       418\n",
      "   macro avg       0.77      0.77      0.77       418\n",
      "weighted avg       0.78      0.78      0.77       418\n",
      "\n"
     ]
    }
   ],
   "source": [
    "rf_clf = RandomForestClassifier(random_state=42)\n",
    "rf_clf.fit(X_train, y_train)\n",
    "\n",
    "y_pred_rf = rf_clf.predict(X_test)\n",
    "\n",
    "print(\"Random Forest:\")\n",
    "print(classification_report(y_test, y_pred_rf))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed7f6f93-f342-4336-af44-904ed903232f",
   "metadata": {},
   "source": [
    "## 3.4 Support Vector Machine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "c28c601e-6a8e-4dde-bad7-217750a69bd6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Support Vector Machine:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.81      0.81      0.81        53\n",
      "           1       0.63      0.67      0.65        57\n",
      "           2       0.62      0.69      0.65        70\n",
      "           3       0.76      0.95      0.84        60\n",
      "           4       0.97      0.98      0.98        65\n",
      "           5       0.70      0.56      0.63        55\n",
      "           6       0.64      0.47      0.54        58\n",
      "\n",
      "    accuracy                           0.74       418\n",
      "   macro avg       0.73      0.73      0.73       418\n",
      "weighted avg       0.73      0.74      0.73       418\n",
      "\n"
     ]
    }
   ],
   "source": [
    "svm_clf = SVC()\n",
    "svm_clf.fit(X_train, y_train)\n",
    "\n",
    "y_pred_svm = svm_clf.predict(X_test)\n",
    "\n",
    "print(\"Support Vector Machine:\")\n",
    "print(classification_report(y_test, y_pred_svm))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "931a3291-f334-4c7e-adc7-d27bb317ca91",
   "metadata": {},
   "source": [
    "## 3.5 K-Nearest Neighbors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "3277fcee-fc6a-4f98-96e9-542aa68043dd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "K-Nearest Neighbors:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.72      0.81      0.76        53\n",
      "           1       0.60      0.37      0.46        57\n",
      "           2       0.56      0.70      0.62        70\n",
      "           3       0.68      0.88      0.77        60\n",
      "           4       0.84      0.98      0.91        65\n",
      "           5       0.52      0.47      0.50        55\n",
      "           6       0.72      0.40      0.51        58\n",
      "\n",
      "    accuracy                           0.67       418\n",
      "   macro avg       0.66      0.66      0.65       418\n",
      "weighted avg       0.66      0.67      0.65       418\n",
      "\n"
     ]
    }
   ],
   "source": [
    "knn_clf = KNeighborsClassifier()\n",
    "knn_clf.fit(X_train, y_train)\n",
    "\n",
    "y_pred_knn = knn_clf.predict(X_test)\n",
    "\n",
    "print(\"K-Nearest Neighbors:\")\n",
    "print(classification_report(y_test, y_pred_knn))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "505751e5-a41c-438f-973a-edfcf20d93d1",
   "metadata": {},
   "source": [
    "## 3.6 Naive Bayes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "043db5d6-3b8d-4653-9a86-4599bfa328b9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Naive Bayes:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.46      0.70      0.55        53\n",
      "           1       0.58      0.32      0.41        57\n",
      "           2       0.55      0.56      0.55        70\n",
      "           3       0.38      0.95      0.55        60\n",
      "           4       0.93      0.98      0.96        65\n",
      "           5       0.42      0.09      0.15        55\n",
      "           6       0.60      0.05      0.10        58\n",
      "\n",
      "    accuracy                           0.53       418\n",
      "   macro avg       0.56      0.52      0.47       418\n",
      "weighted avg       0.57      0.53      0.48       418\n",
      "\n"
     ]
    }
   ],
   "source": [
    "nb_clf = GaussianNB()\n",
    "nb_clf.fit(X_train, y_train)\n",
    "\n",
    "y_pred_nb = nb_clf.predict(X_test)\n",
    "\n",
    "print(\"Naive Bayes:\")\n",
    "print(classification_report(y_test, y_pred_nb))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7590322-6654-427c-ba53-81d76cd81fbd",
   "metadata": {},
   "source": [
    "## 3.7 XGBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "75f37d95-59ac-4571-b8ee-f986f0879fab",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\ProgramSoftware\\anaconda3\\envs\\python8\\lib\\site-packages\\xgboost\\core.py:158: UserWarning: [17:11:35] WARNING: C:\\b\\abs_90_bwj_86a\\croot\\xgboost-split_1724073762025\\work\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  warnings.warn(smsg, UserWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "XGBoost Classifier:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.77      0.87      0.81        53\n",
      "           1       0.54      0.56      0.55        57\n",
      "           2       0.71      0.80      0.75        70\n",
      "           3       0.90      0.93      0.92        60\n",
      "           4       0.98      0.98      0.98        65\n",
      "           5       0.70      0.64      0.67        55\n",
      "           6       0.79      0.59      0.67        58\n",
      "\n",
      "    accuracy                           0.77       418\n",
      "   macro avg       0.77      0.77      0.77       418\n",
      "weighted avg       0.77      0.77      0.77       418\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Fix the category type in X_train/X_test\n",
    "X_train = X_train.apply(lambda col: col.cat.codes if col.dtype.name == 'category' else col)\n",
    "X_test = X_test.apply(lambda col: col.cat.codes if col.dtype.name == 'category' else col)\n",
    "xgb_clf = XGBClassifier(use_label_encoder=False, eval_metric='mlogloss')\n",
    "xgb_clf.fit(X_train, y_train)\n",
    "\n",
    "y_pred_xgb = xgb_clf.predict(X_test)\n",
    "\n",
    "print(\"XGBoost Classifier:\")\n",
    "print(classification_report(y_test, y_pred_xgb))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb090326-bb0f-47cb-a725-9b19e8949fb7",
   "metadata": {},
   "source": [
    "# 4 model comparison "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "9b0e9b85-741f-4c2b-b2f1-329b3b7e3117",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "results = {\n",
    "    'Model': ['Logistic Regression', 'Random Forest', 'SVM', 'KNN', 'Naive Bayes', 'XGBoost'],\n",
    "    'Accuracy': [0.63, 0.78, 0.74, 0.67, 0.53, 0.77],\n",
    "    'Macro F1': [0.60, 0.77, 0.73, 0.65, 0.47, 0.77],\n",
    "    'Weighted F1': [0.61, 0.77, 0.73, 0.65, 0.48, 0.77]\n",
    "}\n",
    "df_results = pd.DataFrame(results)\n",
    "df_results.set_index('Model').plot(kind='bar', figsize=(10, 6))\n",
    "plt.title('Model Comparison')\n",
    "plt.ylabel('Score')\n",
    "plt.ylim(0, 1)\n",
    "plt.legend(loc='lower right')\n",
    "plt.xticks(rotation=15)\n",
    "plt.grid(axis='y', linestyle='--', alpha=0.7)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "bcb3ab1c-3edf-413b-9cbf-355493c045f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x1000 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 所有模型的预测结果（你之前已经生成）\n",
    "models = {\n",
    "    \"Logistic Regression\": y_pred_log,\n",
    "    \"Random Forest\": y_pred_rf,\n",
    "    \"SVM\": y_pred_svm,\n",
    "    \"KNN\": y_pred_knn,\n",
    "    \"Naive Bayes\": y_pred_nb,\n",
    "    \"XGBoost\": y_pred_xgb\n",
    "}\n",
    "\n",
    "# 标签类名（字符串）\n",
    "class_names = le.classes_\n",
    "labels = list(range(len(class_names)))\n",
    "\n",
    "# 创建子图画布\n",
    "fig, axes = plt.subplots(2, 3, figsize=(18, 10))  # 2 行 3 列\n",
    "axes = axes.flatten()\n",
    "\n",
    "# 绘制每个模型的混淆矩阵\n",
    "for i, (model_name, y_pred) in enumerate(models.items()):\n",
    "    cm = confusion_matrix(y_test, y_pred, labels=labels)\n",
    "    disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=class_names)\n",
    "    disp.plot(ax=axes[i], xticks_rotation=45, cmap='Blues', colorbar=False)\n",
    "    axes[i].set_title(model_name)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "670736c7-c0b3-4f03-8ed0-85dbd027e697",
   "metadata": {},
   "source": [
    "Random Forest and SVM demonstrated the best performance, both achieving **over 74% accuracy and F1-score**.\n",
    "\n",
    "Naive Bayes showed the poorest results, likely due to its **feature independence assumption** which doesn't hold true for this dataset.\n",
    "\n",
    "Though a linear model, Logistic Regression delivered **moderate performance** in this multi-class scenario.\n",
    "\n",
    "KNN achieved **acceptable recall rates**, but overall proved less stable than tree-based models.\n",
    "\n",
    "XGBoost matched SVM's performance with **strong generalization capability**, making it the **preferred choice for deployment**."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.20"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
